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'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
lowercase__ = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
for attribute in key.split('.' ):
UpperCAmelCase : Tuple = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
if weight_type is not None:
UpperCAmelCase : List[str] = getattr(UpperCAmelCase_ , UpperCAmelCase_ ).shape
else:
UpperCAmelCase : Union[str, Any] = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
UpperCAmelCase : Optional[Any] = value
elif weight_type == "weight_g":
UpperCAmelCase : Union[str, Any] = value
elif weight_type == "weight_v":
UpperCAmelCase : str = value
elif weight_type == "bias":
UpperCAmelCase : str = value
else:
UpperCAmelCase : str = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = []
UpperCAmelCase : Optional[int] = fairseq_model.state_dict()
UpperCAmelCase : List[str] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
UpperCAmelCase : Dict = None
for name, value in fairseq_dict.items():
UpperCAmelCase : List[Any] = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , hf_model.config.feat_extract_norm == 'group' , )
UpperCAmelCase : Any = True
elif name.split('.' )[0] == "proj":
UpperCAmelCase : List[Any] = fairseq_model.proj
UpperCAmelCase : Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
UpperCAmelCase : Optional[Any] = True
if "*" in mapped_key:
UpperCAmelCase : int = name.split(UpperCAmelCase_ )[0].split('.' )[-2]
UpperCAmelCase : Union[str, Any] = mapped_key.replace('*' , UpperCAmelCase_ )
if "weight_g" in name:
UpperCAmelCase : Optional[Any] = 'weight_g'
elif "weight_v" in name:
UpperCAmelCase : Any = 'weight_v'
elif "bias" in name:
UpperCAmelCase : Dict = 'bias'
elif "weight" in name:
UpperCAmelCase : Optional[int] = 'weight'
else:
UpperCAmelCase : Union[str, Any] = None
set_recursively(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase_ )
logger.warning(F"""Unused weights: {unused_weights}""" )
return proj_weight
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : str = full_name.split('conv_layers.' )[-1]
UpperCAmelCase : Optional[Any] = name.split('.' )
UpperCAmelCase : List[str] = int(items[0] )
UpperCAmelCase : Tuple = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
UpperCAmelCase : int = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
UpperCAmelCase : Optional[int] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
UpperCAmelCase : List[str] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
UpperCAmelCase : List[Any] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCAmelCase_ )
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = emb.weight.shape
UpperCAmelCase : List[str] = nn.Linear(UpperCAmelCase_ , UpperCAmelCase_ , bias=UpperCAmelCase_ )
UpperCAmelCase : Optional[int] = emb.weight.data
return lin_layer
def UpperCamelCase( UpperCAmelCase_ ):
with open(UpperCAmelCase_ , 'r' , encoding='utf-8' ) as f:
UpperCAmelCase : Dict = f.readlines()
UpperCAmelCase : List[str] = [line.split(' ' )[0] for line in lines]
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = {
'<s>': 0,
'<pad>': 1,
'</s>': 2,
'<unk>': 3,
}
vocab_dict.update(dict(zip(UpperCAmelCase_ , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
UpperCAmelCase : List[str] = WavaVecaConfig.from_pretrained(UpperCAmelCase_ )
UpperCAmelCase : Optional[Any] = SpeechaTextaConfig.from_pretrained(
UpperCAmelCase_ , vocab_size=UpperCAmelCase_ , decoder_layers=UpperCAmelCase_ , do_stable_layer_norm=UpperCAmelCase_ )
UpperCAmelCase : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , )
UpperCAmelCase : Dict = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
UpperCAmelCase : Optional[Any] = model[0].eval()
# set weights for wav2vec2 encoder
UpperCAmelCase : Any = WavaVecaModel(UpperCAmelCase_ )
UpperCAmelCase : str = recursively_load_weights_wavaveca(model.encoder , UpperCAmelCase_ )
UpperCAmelCase : int = SpeechaTextaForCausalLM(UpperCAmelCase_ )
UpperCAmelCase : List[Any] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=UpperCAmelCase_ )
# set output linear layer
unexpected_keys.remove('embed_out' )
UpperCAmelCase : str = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
UpperCAmelCase : List[str] = SpeechEncoderDecoderModel(encoder=UpperCAmelCase_ , decoder=UpperCAmelCase_ )
UpperCAmelCase : Any = False
# add projection layer
UpperCAmelCase : Optional[Any] = nn.Parameter(projection_layer.weight )
UpperCAmelCase : List[str] = nn.Parameter(projection_layer.bias )
UpperCAmelCase : Tuple = create_vocab_dict(UpperCAmelCase_ )
with open(os.path.join(UpperCAmelCase_ , 'vocab.json' ) , 'w' ) as fp:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCAmelCase : Optional[int] = SpeechaTextaTokenizer(os.path.join(UpperCAmelCase_ , 'vocab.json' ) )
tokenizer.save_pretrained(UpperCAmelCase_ )
UpperCAmelCase : str = hf_wavavec.config.to_dict()
UpperCAmelCase : int = tokenizer.pad_token_id
UpperCAmelCase : str = tokenizer.bos_token_id
UpperCAmelCase : Union[str, Any] = tokenizer.eos_token_id
UpperCAmelCase : Any = 'speech_to_text_2'
UpperCAmelCase : Any = 'wav2vec2'
UpperCAmelCase : str = SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase_ )
hf_wavavec.save_pretrained(UpperCAmelCase_ )
feature_extractor.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=10224, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
lowercase__ = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 717 |
'''simple docstring'''
from datetime import datetime
import requests
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Tuple = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
UpperCAmelCase : List[str] = requests.get(base_url + url ).json()[0]['urls'][0]['src']
return requests.get(UpperCAmelCase_ ).content
if __name__ == "__main__":
lowercase__ = input("Enter Video/IGTV url: ").strip()
lowercase__ = f'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(f'''Done. Video saved to disk as {file_name}.''')
| 695 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class A_ ( _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = FlaxAutoencoderKL
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
UpperCAmelCase : Dict = 4
UpperCAmelCase : Dict = 3
UpperCAmelCase : List[Any] = (32, 32)
UpperCAmelCase : Dict = jax.random.PRNGKey(0 )
UpperCAmelCase : int = jax.random.uniform(lowercase_ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def UpperCAmelCase_ ( self : Any ) -> Tuple:
UpperCAmelCase : Tuple = {
'block_out_channels': [32, 64],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 4,
}
UpperCAmelCase : str = self.dummy_input
return init_dict, inputs_dict
| 718 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ = 10**9 ):
UpperCAmelCase : Union[str, Any] = 1
UpperCAmelCase : Optional[int] = 2
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = 0
UpperCAmelCase : List[Any] = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
UpperCAmelCase : Dict = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f'''{solution() = }''')
| 695 | 0 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
lowercase__ = True
except ImportError:
lowercase__ = False
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCamelCase( UpperCAmelCase_ ):
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class A_ ( _snake_case ):
'''simple docstring'''
@staticmethod
def UpperCAmelCase_ ( lowercase_ : ArgumentParser ) -> List[Any]:
UpperCAmelCase : Union[str, Any] = parser.add_parser('add-new-model' )
add_new_model_parser.add_argument('--testing' , action='store_true' , help='If in testing mode.' )
add_new_model_parser.add_argument('--testing_file' , type=lowercase_ , help='Configuration file on which to run.' )
add_new_model_parser.add_argument(
'--path' , type=lowercase_ , help='Path to cookiecutter. Should only be used for testing purposes.' )
add_new_model_parser.set_defaults(func=lowercase_ )
def __init__( self : List[Any] , lowercase_ : bool , lowercase_ : str , lowercase_ : Any=None , *lowercase_ : List[Any] ) -> Optional[int]:
UpperCAmelCase : Optional[int] = testing
UpperCAmelCase : Union[str, Any] = testing_file
UpperCAmelCase : Optional[Any] = path
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
warnings.warn(
'The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '
'It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '
'checks, you should use `transformers-cli add-new-model-like` instead.' )
if not _has_cookiecutter:
raise ImportError(
'Model creation dependencies are required to use the `add_new_model` command. Install them by running '
'the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n' )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
UpperCAmelCase : str = [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
if len(lowercase_ ) > 0:
raise ValueError(
'Several directories starting with `cookiecutter-template-` in current working directory. '
'Please clean your directory by removing all folders starting with `cookiecutter-template-` or '
'change your working directory.' )
UpperCAmelCase : List[str] = (
Path(lowercase_ ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
UpperCAmelCase : List[Any] = path_to_transformer_root / 'templates' / 'adding_a_new_model'
# Execute cookiecutter
if not self._testing:
cookiecutter(str(lowercase_ ) )
else:
with open(self._testing_file , 'r' ) as configuration_file:
UpperCAmelCase : Dict = json.load(lowercase_ )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) , no_input=lowercase_ , extra_context=lowercase_ , )
UpperCAmelCase : str = [directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]
# Retrieve configuration
with open(directory + '/configuration.json' , 'r' ) as configuration_file:
UpperCAmelCase : Any = json.load(lowercase_ )
UpperCAmelCase : Optional[Any] = configuration['lowercase_modelname']
UpperCAmelCase : Union[str, Any] = configuration['generate_tensorflow_pytorch_and_flax']
os.remove(f"""{directory}/configuration.json""" )
UpperCAmelCase : Tuple = 'PyTorch' in generate_tensorflow_pytorch_and_flax
UpperCAmelCase : List[str] = 'TensorFlow' in generate_tensorflow_pytorch_and_flax
UpperCAmelCase : List[Any] = 'Flax' in generate_tensorflow_pytorch_and_flax
UpperCAmelCase : Tuple = f"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
os.makedirs(lowercase_ , exist_ok=lowercase_ )
os.makedirs(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}""" , exist_ok=lowercase_ )
# Tests require submodules as they have parent imports
with open(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py""" , 'w' ):
pass
shutil.move(
f"""{directory}/__init__.py""" , f"""{model_dir}/__init__.py""" , )
shutil.move(
f"""{directory}/configuration_{lowercase_model_name}.py""" , f"""{model_dir}/configuration_{lowercase_model_name}.py""" , )
def remove_copy_lines(lowercase_ : Tuple ):
with open(lowercase_ , 'r' ) as f:
UpperCAmelCase : Any = f.readlines()
with open(lowercase_ , 'w' ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(lowercase_ )
if output_pytorch:
if not self._testing:
remove_copy_lines(f"""{directory}/modeling_{lowercase_model_name}.py""" )
shutil.move(
f"""{directory}/modeling_{lowercase_model_name}.py""" , f"""{model_dir}/modeling_{lowercase_model_name}.py""" , )
shutil.move(
f"""{directory}/test_modeling_{lowercase_model_name}.py""" , f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py""" , )
else:
os.remove(f"""{directory}/modeling_{lowercase_model_name}.py""" )
os.remove(f"""{directory}/test_modeling_{lowercase_model_name}.py""" )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f"""{directory}/modeling_tf_{lowercase_model_name}.py""" )
shutil.move(
f"""{directory}/modeling_tf_{lowercase_model_name}.py""" , f"""{model_dir}/modeling_tf_{lowercase_model_name}.py""" , )
shutil.move(
f"""{directory}/test_modeling_tf_{lowercase_model_name}.py""" , f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py""" , )
else:
os.remove(f"""{directory}/modeling_tf_{lowercase_model_name}.py""" )
os.remove(f"""{directory}/test_modeling_tf_{lowercase_model_name}.py""" )
if output_flax:
if not self._testing:
remove_copy_lines(f"""{directory}/modeling_flax_{lowercase_model_name}.py""" )
shutil.move(
f"""{directory}/modeling_flax_{lowercase_model_name}.py""" , f"""{model_dir}/modeling_flax_{lowercase_model_name}.py""" , )
shutil.move(
f"""{directory}/test_modeling_flax_{lowercase_model_name}.py""" , f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py""" , )
else:
os.remove(f"""{directory}/modeling_flax_{lowercase_model_name}.py""" )
os.remove(f"""{directory}/test_modeling_flax_{lowercase_model_name}.py""" )
shutil.move(
f"""{directory}/{lowercase_model_name}.md""" , f"""{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md""" , )
shutil.move(
f"""{directory}/tokenization_{lowercase_model_name}.py""" , f"""{model_dir}/tokenization_{lowercase_model_name}.py""" , )
shutil.move(
f"""{directory}/tokenization_fast_{lowercase_model_name}.py""" , f"""{model_dir}/tokenization_{lowercase_model_name}_fast.py""" , )
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(lowercase_ : str , lowercase_ : str , lowercase_ : List[str] ):
# Create temp file
UpperCAmelCase : Tuple = mkstemp()
UpperCAmelCase : Dict = False
with fdopen(lowercase_ , 'w' ) as new_file:
with open(lowercase_ ) as old_file:
for line in old_file:
new_file.write(lowercase_ )
if line_to_copy_below in line:
UpperCAmelCase : Union[str, Any] = True
for line_to_copy in lines_to_copy:
new_file.write(lowercase_ )
if not line_found:
raise ValueError(f"""Line {line_to_copy_below} was not found in file.""" )
# Copy the file permissions from the old file to the new file
copymode(lowercase_ , lowercase_ )
# Remove original file
remove(lowercase_ )
# Move new file
move(lowercase_ , lowercase_ )
def skip_units(lowercase_ : int ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(lowercase_ : Optional[Any] ):
with open(lowercase_ ) as datafile:
UpperCAmelCase : Dict = []
UpperCAmelCase : Optional[int] = False
UpperCAmelCase : str = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
UpperCAmelCase : List[str] = line.split('"' )[1]
UpperCAmelCase : Tuple = skip_units(lowercase_ )
elif "# Below: " in line and "##" not in line:
UpperCAmelCase : str = line.split('"' )[1]
UpperCAmelCase : Union[str, Any] = skip_units(lowercase_ )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase : str = []
elif "# Replace with" in line and "##" not in line:
UpperCAmelCase : Tuple = []
elif "##" not in line:
lines_to_copy.append(lowercase_ )
remove(lowercase_ )
replace_in_files(f"""{directory}/to_replace_{lowercase_model_name}.py""" )
os.rmdir(lowercase_ )
| 719 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( unittest.TestCase ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self : Any ) -> List[Any]:
torch.manual_seed(0 )
UpperCAmelCase : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> int:
torch.manual_seed(0 )
UpperCAmelCase : str = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , )
return model
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> List[str]:
torch.manual_seed(0 )
UpperCAmelCase : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(lowercase_ )
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
UpperCAmelCase : Any = self.dummy_uncond_unet
UpperCAmelCase : Tuple = DDIMScheduler()
UpperCAmelCase : Optional[Any] = self.dummy_vq_model
UpperCAmelCase : str = LDMPipeline(unet=lowercase_ , vqvae=lowercase_ , scheduler=lowercase_ )
ldm.to(lowercase_ )
ldm.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : str = torch.manual_seed(0 )
UpperCAmelCase : int = ldm(generator=lowercase_ , num_inference_steps=2 , output_type='numpy' ).images
UpperCAmelCase : int = torch.manual_seed(0 )
UpperCAmelCase : Tuple = ldm(generator=lowercase_ , num_inference_steps=2 , output_type='numpy' , return_dict=lowercase_ )[0]
UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : List[str] = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
UpperCAmelCase : Tuple = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Tuple ) -> Any:
UpperCAmelCase : Any = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(lowercase_ )
ldm.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Tuple = torch.manual_seed(0 )
UpperCAmelCase : Dict = ldm(generator=lowercase_ , num_inference_steps=5 , output_type='numpy' ).images
UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Optional[int] = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
UpperCAmelCase : Any = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 695 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 720 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( unittest.TestCase ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
torch.manual_seed(0 )
UpperCAmelCase : Any = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
UpperCAmelCase : Dict = self.dummy_uncond_unet
UpperCAmelCase : Dict = KarrasVeScheduler()
UpperCAmelCase : str = KarrasVePipeline(unet=lowercase_ , scheduler=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = pipe(num_inference_steps=2 , generator=lowercase_ , output_type='numpy' ).images
UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
UpperCAmelCase : Optional[Any] = pipe(num_inference_steps=2 , generator=lowercase_ , output_type='numpy' , return_dict=lowercase_ )[0]
UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : Any = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple:
UpperCAmelCase : Dict = 'google/ncsnpp-celebahq-256'
UpperCAmelCase : Any = UNetaDModel.from_pretrained(lowercase_ )
UpperCAmelCase : Union[str, Any] = KarrasVeScheduler()
UpperCAmelCase : Dict = KarrasVePipeline(unet=lowercase_ , scheduler=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Union[str, Any] = torch.manual_seed(0 )
UpperCAmelCase : Dict = pipe(num_inference_steps=20 , generator=lowercase_ , output_type='numpy' ).images
UpperCAmelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Optional[int] = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 695 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_snake_case )
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : str = field(default="""image-classification""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
UpperCAmelCase_ : ClassVar[Features] = Features({"""image""": Image()} )
UpperCAmelCase_ : ClassVar[Features] = Features({"""labels""": ClassLabel} )
UpperCAmelCase_ : str = "image"
UpperCAmelCase_ : str = "labels"
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : Dict ) -> Optional[Any]:
if self.label_column not in features:
raise ValueError(f"""Column {self.label_column} is not present in features.""" )
if not isinstance(features[self.label_column] , lowercase_ ):
raise ValueError(f"""Column {self.label_column} is not a ClassLabel.""" )
UpperCAmelCase : int = copy.deepcopy(self )
UpperCAmelCase : str = self.label_schema.copy()
UpperCAmelCase : Optional[int] = features[self.label_column]
UpperCAmelCase : int = label_schema
return task_template
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Dict[str, str]:
return {
self.image_column: "image",
self.label_column: "labels",
}
| 721 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = """autoformer"""
UpperCAmelCase_ : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__( self : Dict , lowercase_ : Optional[int] = None , lowercase_ : Optional[int] = None , lowercase_ : str = "student_t" , lowercase_ : str = "nll" , lowercase_ : int = 1 , lowercase_ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowercase_ : bool = True , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : Optional[List[int]] = None , lowercase_ : Optional[List[int]] = None , lowercase_ : int = 64 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : int = 32 , lowercase_ : int = 32 , lowercase_ : str = "gelu" , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : int = 100 , lowercase_ : float = 0.02 , lowercase_ : bool = True , lowercase_ : Union[str, Any]=True , lowercase_ : int = 10 , lowercase_ : int = 25 , lowercase_ : int = 3 , **lowercase_ : str , ) -> Dict:
# time series specific configuration
UpperCAmelCase : int = prediction_length
UpperCAmelCase : Optional[Any] = context_length if context_length is not None else prediction_length
UpperCAmelCase : List[Any] = distribution_output
UpperCAmelCase : Tuple = loss
UpperCAmelCase : Dict = input_size
UpperCAmelCase : Dict = num_time_features
UpperCAmelCase : Tuple = lags_sequence
UpperCAmelCase : str = scaling
UpperCAmelCase : Optional[int] = num_dynamic_real_features
UpperCAmelCase : List[str] = num_static_real_features
UpperCAmelCase : Optional[int] = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(lowercase_ ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
UpperCAmelCase : int = cardinality
else:
UpperCAmelCase : Union[str, Any] = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(lowercase_ ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
UpperCAmelCase : Any = embedding_dimension
else:
UpperCAmelCase : int = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
UpperCAmelCase : Dict = num_parallel_samples
# Transformer architecture configuration
UpperCAmelCase : Optional[int] = input_size * len(self.lags_sequence ) + self._number_of_features
UpperCAmelCase : List[Any] = d_model
UpperCAmelCase : Dict = encoder_attention_heads
UpperCAmelCase : Tuple = decoder_attention_heads
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : str = decoder_ffn_dim
UpperCAmelCase : str = encoder_layers
UpperCAmelCase : Optional[Any] = decoder_layers
UpperCAmelCase : int = dropout
UpperCAmelCase : Any = attention_dropout
UpperCAmelCase : Tuple = activation_dropout
UpperCAmelCase : str = encoder_layerdrop
UpperCAmelCase : Union[str, Any] = decoder_layerdrop
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : Dict = init_std
UpperCAmelCase : Union[str, Any] = use_cache
# Autoformer
UpperCAmelCase : Any = label_length
UpperCAmelCase : List[Any] = moving_average
UpperCAmelCase : Optional[Any] = autocorrelation_factor
super().__init__(is_encoder_decoder=lowercase_ , **lowercase_ )
@property
def UpperCAmelCase_ ( self : List[str] ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 695 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : Dict = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : str = """gpt_bigcode"""
UpperCAmelCase_ : List[Any] = ["""past_key_values"""]
UpperCAmelCase_ : Union[str, Any] = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Optional[Any] , lowercase_ : Dict=50_257 , lowercase_ : Optional[Any]=1_024 , lowercase_ : str=768 , lowercase_ : List[Any]=12 , lowercase_ : List[Any]=12 , lowercase_ : int=None , lowercase_ : List[Any]="gelu_pytorch_tanh" , lowercase_ : Optional[int]=0.1 , lowercase_ : Any=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : Any=1E-5 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : List[Any]=True , lowercase_ : Tuple=True , lowercase_ : Optional[int]=50_256 , lowercase_ : Tuple=50_256 , lowercase_ : List[str]=True , lowercase_ : Any=True , lowercase_ : str=True , **lowercase_ : int , ) -> Optional[int]:
UpperCAmelCase : List[str] = vocab_size
UpperCAmelCase : Optional[int] = n_positions
UpperCAmelCase : Tuple = n_embd
UpperCAmelCase : Any = n_layer
UpperCAmelCase : str = n_head
UpperCAmelCase : Dict = n_inner
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : Optional[Any] = resid_pdrop
UpperCAmelCase : Tuple = embd_pdrop
UpperCAmelCase : int = attn_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : Optional[Any] = scale_attn_weights
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : Tuple = attention_softmax_in_fpaa
UpperCAmelCase : List[Any] = scale_attention_softmax_in_fpaa
UpperCAmelCase : Optional[int] = multi_query
UpperCAmelCase : List[str] = bos_token_id
UpperCAmelCase : Any = eos_token_id
super().__init__(bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
| 700 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return [sentence[i : i + ngram_size] for i in range(len(UpperCAmelCase_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 695 | 0 |
'''simple docstring'''
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
lowercase__ = "Create a default config file for Accelerate with only a few flags set."
def UpperCamelCase( UpperCAmelCase_="no" , UpperCAmelCase_ = default_json_config_file , UpperCAmelCase_ = False ):
UpperCAmelCase : Any = Path(UpperCAmelCase_ )
path.parent.mkdir(parents=UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
if path.exists():
print(
F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
UpperCAmelCase : Optional[int] = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
UpperCAmelCase : Dict = {
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
UpperCAmelCase : Dict = torch.cuda.device_count()
UpperCAmelCase : List[Any] = num_gpus
UpperCAmelCase : List[Any] = False
if num_gpus > 1:
UpperCAmelCase : Tuple = 'MULTI_GPU'
else:
UpperCAmelCase : Optional[Any] = 'NO'
elif is_xpu_available() and use_xpu:
UpperCAmelCase : Optional[int] = torch.xpu.device_count()
UpperCAmelCase : Optional[int] = num_xpus
UpperCAmelCase : Any = False
if num_xpus > 1:
UpperCAmelCase : Tuple = 'MULTI_XPU'
else:
UpperCAmelCase : str = 'NO'
elif is_npu_available():
UpperCAmelCase : Optional[int] = torch.npu.device_count()
UpperCAmelCase : str = num_npus
UpperCAmelCase : int = False
if num_npus > 1:
UpperCAmelCase : int = 'MULTI_NPU'
else:
UpperCAmelCase : List[str] = 'NO'
else:
UpperCAmelCase : str = 0
UpperCAmelCase : int = True
UpperCAmelCase : str = 1
UpperCAmelCase : str = 'NO'
UpperCAmelCase : Any = ClusterConfig(**UpperCAmelCase_ )
config.to_json_file(UpperCAmelCase_ )
return path
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = parser.add_parser('default' , parents=UpperCAmelCase_ , help=UpperCAmelCase_ , formatter_class=UpperCAmelCase_ )
parser.add_argument(
'--config_file' , default=UpperCAmelCase_ , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , dest='save_location' , )
parser.add_argument(
'--mixed_precision' , choices=['no', 'fp16', 'bf16'] , type=UpperCAmelCase_ , help='Whether or not to use mixed precision training. '
'Choose between FP16 and BF16 (bfloat16) training. '
'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' , default='no' , )
parser.set_defaults(func=UpperCAmelCase_ )
return parser
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F"""accelerate configuration saved at {config_file}""" )
| 701 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 1 / sqrt(2 ) ):
UpperCAmelCase : int = tau * frequency / samplerate
UpperCAmelCase : int = sin(UpperCAmelCase_ )
UpperCAmelCase : Optional[int] = cos(UpperCAmelCase_ )
UpperCAmelCase : Dict = _sin / (2 * q_factor)
UpperCAmelCase : Tuple = (1 - _cos) / 2
UpperCAmelCase : Dict = 1 - _cos
UpperCAmelCase : Union[str, Any] = 1 + alpha
UpperCAmelCase : List[str] = -2 * _cos
UpperCAmelCase : List[Any] = 1 - alpha
UpperCAmelCase : Tuple = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 1 / sqrt(2 ) ):
UpperCAmelCase : Tuple = tau * frequency / samplerate
UpperCAmelCase : List[Any] = sin(UpperCAmelCase_ )
UpperCAmelCase : List[str] = cos(UpperCAmelCase_ )
UpperCAmelCase : Tuple = _sin / (2 * q_factor)
UpperCAmelCase : int = (1 + _cos) / 2
UpperCAmelCase : List[Any] = -1 - _cos
UpperCAmelCase : Optional[int] = 1 + alpha
UpperCAmelCase : Any = -2 * _cos
UpperCAmelCase : str = 1 - alpha
UpperCAmelCase : Dict = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 1 / sqrt(2 ) ):
UpperCAmelCase : List[Any] = tau * frequency / samplerate
UpperCAmelCase : Union[str, Any] = sin(UpperCAmelCase_ )
UpperCAmelCase : List[Any] = cos(UpperCAmelCase_ )
UpperCAmelCase : Any = _sin / (2 * q_factor)
UpperCAmelCase : Optional[Any] = _sin / 2
UpperCAmelCase : Union[str, Any] = 0
UpperCAmelCase : int = -ba
UpperCAmelCase : List[str] = 1 + alpha
UpperCAmelCase : int = -2 * _cos
UpperCAmelCase : Optional[int] = 1 - alpha
UpperCAmelCase : List[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 1 / sqrt(2 ) ):
UpperCAmelCase : int = tau * frequency / samplerate
UpperCAmelCase : Tuple = sin(UpperCAmelCase_ )
UpperCAmelCase : Dict = cos(UpperCAmelCase_ )
UpperCAmelCase : int = _sin / (2 * q_factor)
UpperCAmelCase : int = 1 - alpha
UpperCAmelCase : Dict = -2 * _cos
UpperCAmelCase : Any = 1 + alpha
UpperCAmelCase : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 1 / sqrt(2 ) , ):
UpperCAmelCase : Optional[int] = tau * frequency / samplerate
UpperCAmelCase : List[Any] = sin(UpperCAmelCase_ )
UpperCAmelCase : Dict = cos(UpperCAmelCase_ )
UpperCAmelCase : Optional[Any] = _sin / (2 * q_factor)
UpperCAmelCase : Union[str, Any] = 10 ** (gain_db / 40)
UpperCAmelCase : int = 1 + alpha * big_a
UpperCAmelCase : Tuple = -2 * _cos
UpperCAmelCase : List[Any] = 1 - alpha * big_a
UpperCAmelCase : Tuple = 1 + alpha / big_a
UpperCAmelCase : Tuple = -2 * _cos
UpperCAmelCase : int = 1 - alpha / big_a
UpperCAmelCase : List[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 1 / sqrt(2 ) , ):
UpperCAmelCase : Dict = tau * frequency / samplerate
UpperCAmelCase : List[str] = sin(UpperCAmelCase_ )
UpperCAmelCase : Any = cos(UpperCAmelCase_ )
UpperCAmelCase : str = _sin / (2 * q_factor)
UpperCAmelCase : List[str] = 10 ** (gain_db / 40)
UpperCAmelCase : str = (big_a + 1) - (big_a - 1) * _cos
UpperCAmelCase : Dict = (big_a + 1) + (big_a - 1) * _cos
UpperCAmelCase : int = (big_a - 1) - (big_a + 1) * _cos
UpperCAmelCase : Tuple = (big_a - 1) + (big_a + 1) * _cos
UpperCAmelCase : List[str] = 2 * sqrt(UpperCAmelCase_ ) * alpha
UpperCAmelCase : List[Any] = big_a * (pmc + aaa)
UpperCAmelCase : Optional[int] = 2 * big_a * mpc
UpperCAmelCase : Optional[int] = big_a * (pmc - aaa)
UpperCAmelCase : str = ppmc + aaa
UpperCAmelCase : int = -2 * pmpc
UpperCAmelCase : int = ppmc - aaa
UpperCAmelCase : Any = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 1 / sqrt(2 ) , ):
UpperCAmelCase : Tuple = tau * frequency / samplerate
UpperCAmelCase : List[str] = sin(UpperCAmelCase_ )
UpperCAmelCase : List[Any] = cos(UpperCAmelCase_ )
UpperCAmelCase : int = _sin / (2 * q_factor)
UpperCAmelCase : List[str] = 10 ** (gain_db / 40)
UpperCAmelCase : str = (big_a + 1) - (big_a - 1) * _cos
UpperCAmelCase : List[Any] = (big_a + 1) + (big_a - 1) * _cos
UpperCAmelCase : int = (big_a - 1) - (big_a + 1) * _cos
UpperCAmelCase : Tuple = (big_a - 1) + (big_a + 1) * _cos
UpperCAmelCase : Optional[Any] = 2 * sqrt(UpperCAmelCase_ ) * alpha
UpperCAmelCase : Dict = big_a * (ppmc + aaa)
UpperCAmelCase : List[str] = -2 * big_a * pmpc
UpperCAmelCase : int = big_a * (ppmc - aaa)
UpperCAmelCase : Dict = pmc + aaa
UpperCAmelCase : Optional[int] = 2 * mpc
UpperCAmelCase : int = pmc - aaa
UpperCAmelCase : Union[str, Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 695 | 0 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 702 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase__ = TypeVar("T")
class A_ ( Generic[T] ):
'''simple docstring'''
UpperCAmelCase_ : deque[T] # Cache store of keys
UpperCAmelCase_ : set[T] # References of the keys in cache
UpperCAmelCase_ : int = 10 # Maximum capacity of cache
def __init__( self : List[Any] , lowercase_ : int ) -> None:
UpperCAmelCase : Any = deque()
UpperCAmelCase : Dict = set()
if not n:
UpperCAmelCase : Optional[int] = sys.maxsize
elif n < 0:
raise ValueError('n should be an integer greater than 0.' )
else:
UpperCAmelCase : str = n
def UpperCAmelCase_ ( self : List[str] , lowercase_ : T ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
UpperCAmelCase : Optional[Any] = self.dq_store.pop()
self.key_reference.remove(lowercase_ )
else:
self.dq_store.remove(lowercase_ )
self.dq_store.appendleft(lowercase_ )
self.key_reference.add(lowercase_ )
def UpperCAmelCase_ ( self : Dict ) -> None:
for k in self.dq_store:
print(lowercase_ )
def __repr__( self : Union[str, Any] ) -> str:
return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ = LRUCache(4)
lru_cache.refer("A")
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer("A")
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 695 | 0 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Tuple = 0
UpperCAmelCase : Any = len(UpperCAmelCase_ ) # No of vertices in graph
UpperCAmelCase : int = [0] * n
UpperCAmelCase : List[str] = [False] * n
def dfs(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Any = True
UpperCAmelCase : List[str] = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , id_ )
UpperCAmelCase : List[str] = min(low[at] , low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
UpperCAmelCase : Union[str, Any] = min(low[at] , low[to] )
UpperCAmelCase : list[tuple[int, int]] = []
for i in range(UpperCAmelCase_ ):
if not visited[i]:
dfs(UpperCAmelCase_ , -1 , UpperCAmelCase_ , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
lowercase__ = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
lowercase__ = {
"gpt-neox-20b": 2048,
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = VOCAB_FILES_NAMES
UpperCAmelCase_ : str = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ : Dict = ["""input_ids""", """attention_mask"""]
def __init__( self : List[str] , lowercase_ : Any=None , lowercase_ : Dict=None , lowercase_ : List[str]=None , lowercase_ : List[Any]="<|endoftext|>" , lowercase_ : List[str]="<|endoftext|>" , lowercase_ : Any="<|endoftext|>" , lowercase_ : List[str]=False , **lowercase_ : Union[str, Any] , ) -> str:
super().__init__(
lowercase_ , lowercase_ , tokenizer_file=lowercase_ , unk_token=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , add_prefix_space=lowercase_ , **lowercase_ , )
UpperCAmelCase : str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , lowercase_ ) != add_prefix_space:
UpperCAmelCase : Tuple = getattr(lowercase_ , pre_tok_state.pop('type' ) )
UpperCAmelCase : Optional[Any] = add_prefix_space
UpperCAmelCase : Tuple = pre_tok_class(**lowercase_ )
UpperCAmelCase : Any = add_prefix_space
def UpperCAmelCase_ ( self : Tuple , lowercase_ : str , lowercase_ : Optional[str] = None ) -> Tuple[str]:
UpperCAmelCase : Optional[int] = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : "Conversation" ) -> List[int]:
UpperCAmelCase : List[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowercase_ , add_special_tokens=lowercase_ ) + [self.eos_token_id] )
if len(lowercase_ ) > self.model_max_length:
UpperCAmelCase : int = input_ids[-self.model_max_length :]
return input_ids
| 695 | 0 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowercase__ = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Any ) -> Optional[Any]:
UpperCAmelCase : Any = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , 'schedulers/' ) )
UpperCAmelCase : Tuple = self.diffusers_dir
shutil.copy(
os.path.join(lowercase_ , 'src/diffusers/schedulers/scheduling_ddpm.py' ) , os.path.join(self.diffusers_dir , 'schedulers/scheduling_ddpm.py' ) , )
def UpperCAmelCase_ ( self : Optional[int] ) -> List[Any]:
UpperCAmelCase : Optional[Any] = 'src/diffusers'
shutil.rmtree(self.diffusers_dir )
def UpperCAmelCase_ ( self : List[str] , lowercase_ : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : str=None ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
UpperCAmelCase : Optional[Any] = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
UpperCAmelCase : int = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
UpperCAmelCase : Union[str, Any] = black.format_str(lowercase_ , mode=lowercase_ )
UpperCAmelCase : List[str] = os.path.join(self.diffusers_dir , 'new_code.py' )
with open(lowercase_ , 'w' , newline='\n' ) as f:
f.write(lowercase_ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(lowercase_ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=lowercase_ )
with open(lowercase_ , 'r' ) as f:
self.assertTrue(f.read() , lowercase_ )
def UpperCAmelCase_ ( self : int ) -> List[Any]:
UpperCAmelCase : List[Any] = check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput' )
self.assertEqual(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : Dict ) -> Any:
# Base copy consistency
self.check_copy_consistency(
'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput' , 'DDPMSchedulerOutput' , REFERENCE_CODE + '\n' , )
# With no empty line at the end
self.check_copy_consistency(
'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput' , 'DDPMSchedulerOutput' , lowercase_ , )
# Copy consistency with rename
self.check_copy_consistency(
'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test' , 'TestSchedulerOutput' , re.sub('DDPM' , 'Test' , lowercase_ ) , )
# Copy consistency with a really long name
UpperCAmelCase : Optional[int] = 'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub('Bert' , lowercase_ , lowercase_ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test' , 'TestSchedulerOutput' , lowercase_ , overwrite_result=re.sub('DDPM' , 'Test' , lowercase_ ) , )
| 704 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = """openai/whisper-base"""
UpperCAmelCase_ : Union[str, Any] = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
UpperCAmelCase_ : Dict = """transcriber"""
UpperCAmelCase_ : int = WhisperProcessor
UpperCAmelCase_ : Optional[int] = WhisperForConditionalGeneration
UpperCAmelCase_ : Dict = ["""audio"""]
UpperCAmelCase_ : Optional[int] = ["""text"""]
def UpperCAmelCase_ ( self : Tuple , lowercase_ : str ) -> Optional[int]:
return self.pre_processor(lowercase_ , return_tensors='pt' ).input_features
def UpperCAmelCase_ ( self : Tuple , lowercase_ : int ) -> List[str]:
return self.model.generate(inputs=lowercase_ )
def UpperCAmelCase_ ( self : str , lowercase_ : List[Any] ) -> List[str]:
return self.pre_processor.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )[0]
| 695 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A_ ( _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = XLMTokenizer
UpperCAmelCase_ : int = False
def UpperCAmelCase_ ( self : Dict ) -> Dict:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase : Dict = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
UpperCAmelCase : Dict = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
UpperCAmelCase : Any = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']
UpperCAmelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
UpperCAmelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(lowercase_ ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(lowercase_ ) )
def UpperCAmelCase_ ( self : Tuple , lowercase_ : Optional[int] ) -> str:
UpperCAmelCase : List[Any] = 'lower newer'
UpperCAmelCase : Optional[int] = 'lower newer'
return input_text, output_text
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
UpperCAmelCase : Tuple = XLMTokenizer(self.vocab_file , self.merges_file )
UpperCAmelCase : Tuple = 'lower'
UpperCAmelCase : Tuple = ['low', 'er</w>']
UpperCAmelCase : Tuple = tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase : Any = tokens + ['<unk>']
UpperCAmelCase : List[str] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ )
@slow
def UpperCAmelCase_ ( self : Any ) -> List[str]:
UpperCAmelCase : Optional[Any] = XLMTokenizer.from_pretrained('xlm-mlm-en-2048' )
UpperCAmelCase : Any = tokenizer.encode('sequence builders' , add_special_tokens=lowercase_ )
UpperCAmelCase : List[str] = tokenizer.encode('multi-sequence build' , add_special_tokens=lowercase_ )
UpperCAmelCase : List[str] = tokenizer.build_inputs_with_special_tokens(lowercase_ )
UpperCAmelCase : Optional[int] = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 705 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
lowercase__ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
lowercase__ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = """whisper"""
UpperCAmelCase_ : Tuple = ["""past_key_values"""]
UpperCAmelCase_ : Union[str, Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : str , lowercase_ : Any=51_865 , lowercase_ : List[Any]=80 , lowercase_ : int=6 , lowercase_ : Dict=4 , lowercase_ : List[Any]=6 , lowercase_ : Any=4 , lowercase_ : Tuple=1_536 , lowercase_ : Tuple=1_536 , lowercase_ : Tuple=0.0 , lowercase_ : Optional[int]=0.0 , lowercase_ : List[Any]=50_257 , lowercase_ : Optional[int]=True , lowercase_ : Any=True , lowercase_ : str="gelu" , lowercase_ : List[str]=256 , lowercase_ : str=0.0 , lowercase_ : Any=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Dict=0.02 , lowercase_ : Optional[int]=False , lowercase_ : Union[str, Any]=1_500 , lowercase_ : List[Any]=448 , lowercase_ : int=50_256 , lowercase_ : Union[str, Any]=50_256 , lowercase_ : List[Any]=50_256 , lowercase_ : Tuple=None , lowercase_ : Optional[Any]=[220, 50_256] , lowercase_ : Tuple=False , lowercase_ : str=256 , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=0.05 , lowercase_ : Any=10 , lowercase_ : Optional[Any]=2 , lowercase_ : Optional[Any]=0.0 , lowercase_ : Optional[int]=10 , lowercase_ : int=0 , lowercase_ : Optional[int]=7 , **lowercase_ : Union[str, Any] , ) -> List[str]:
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Any = num_mel_bins
UpperCAmelCase : List[Any] = d_model
UpperCAmelCase : int = encoder_layers
UpperCAmelCase : str = encoder_attention_heads
UpperCAmelCase : Tuple = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : Tuple = decoder_ffn_dim
UpperCAmelCase : List[str] = encoder_ffn_dim
UpperCAmelCase : int = dropout
UpperCAmelCase : int = attention_dropout
UpperCAmelCase : List[Any] = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : Union[str, Any] = init_std
UpperCAmelCase : Dict = encoder_layerdrop
UpperCAmelCase : str = decoder_layerdrop
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : int = encoder_layers
UpperCAmelCase : int = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Tuple = max_source_positions
UpperCAmelCase : List[Any] = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : Optional[int] = classifier_proj_size
UpperCAmelCase : List[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : Optional[Any] = mask_time_prob
UpperCAmelCase : Optional[Any] = mask_time_length
UpperCAmelCase : str = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Tuple = mask_feature_length
UpperCAmelCase : Optional[int] = mask_feature_min_masks
UpperCAmelCase : str = median_filter_width
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , suppress_tokens=lowercase_ , begin_suppress_tokens=lowercase_ , **lowercase_ , )
class A_ ( _snake_case ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : Optional[int] = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
UpperCAmelCase : int = {0: 'batch'}
else:
UpperCAmelCase : List[str] = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='inputs' )
return common_inputs
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional["TensorType"] = None , lowercase_ : int = 22_050 , lowercase_ : float = 5.0 , lowercase_ : int = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Tuple = OrderedDict()
UpperCAmelCase : Tuple = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=lowercase_ , framework=lowercase_ , sampling_rate=lowercase_ , time_duration=lowercase_ , frequency=lowercase_ , )
UpperCAmelCase : Optional[Any] = encoder_inputs['input_features'].shape[2]
UpperCAmelCase : Tuple = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Optional[int] = super().generate_dummy_inputs(
preprocessor.tokenizer , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase : Dict = encoder_inputs.pop('input_features' )
UpperCAmelCase : List[str] = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def UpperCAmelCase_ ( self : Dict ) -> float:
return 1E-3
| 695 | 0 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class A_ ( _snake_case ):
UpperCAmelCase_ : torch.FloatTensor
UpperCAmelCase_ : Optional[torch.FloatTensor] = None
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_=0.999 , UpperCAmelCase_="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(UpperCAmelCase_ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(UpperCAmelCase_ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
UpperCAmelCase : Optional[Any] = []
for i in range(UpperCAmelCase_ ):
UpperCAmelCase : int = i / num_diffusion_timesteps
UpperCAmelCase : Union[str, Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_ ) / alpha_bar_fn(UpperCAmelCase_ ) , UpperCAmelCase_ ) )
return torch.tensor(UpperCAmelCase_ , dtype=torch.floataa )
class A_ ( _snake_case , _snake_case ):
@register_to_config
def __init__( self : List[Any] , lowercase_ : int = 1_000 , lowercase_ : str = "fixed_small_log" , lowercase_ : bool = True , lowercase_ : Optional[float] = 1.0 , lowercase_ : str = "epsilon" , lowercase_ : str = "squaredcos_cap_v2" , ) -> List[str]:
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )
UpperCAmelCase : Optional[Any] = betas_for_alpha_bar(lowercase_ )
UpperCAmelCase : Optional[Any] = 1.0 - self.betas
UpperCAmelCase : Any = torch.cumprod(self.alphas , dim=0 )
UpperCAmelCase : List[str] = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
UpperCAmelCase : Optional[Any] = 1.0
# setable values
UpperCAmelCase : Optional[Any] = None
UpperCAmelCase : Optional[Any] = torch.from_numpy(np.arange(0 , lowercase_ )[::-1].copy() )
UpperCAmelCase : str = variance_type
def UpperCAmelCase_ ( self : int , lowercase_ : torch.FloatTensor , lowercase_ : Optional[int] = None ) -> torch.FloatTensor:
return sample
def UpperCAmelCase_ ( self : List[Any] , lowercase_ : int , lowercase_ : Union[str, torch.device] = None ) -> str:
UpperCAmelCase : List[Any] = num_inference_steps
UpperCAmelCase : Dict = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
UpperCAmelCase : List[str] = (np.arange(0 , lowercase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
UpperCAmelCase : Dict = torch.from_numpy(lowercase_ ).to(lowercase_ )
def UpperCAmelCase_ ( self : int , lowercase_ : Optional[int] , lowercase_ : Union[str, Any]=None , lowercase_ : List[str]=None , lowercase_ : str=None ) -> str:
if prev_timestep is None:
UpperCAmelCase : int = t - 1
UpperCAmelCase : Optional[int] = self.alphas_cumprod[t]
UpperCAmelCase : Optional[Any] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase : Any = 1 - alpha_prod_t
UpperCAmelCase : Any = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase : Dict = self.betas[t]
else:
UpperCAmelCase : str = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase : List[str] = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
UpperCAmelCase : Union[str, Any] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
UpperCAmelCase : Dict = torch.log(torch.clamp(lowercase_ , min=1E-20 ) )
UpperCAmelCase : int = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
UpperCAmelCase : int = variance.log()
UpperCAmelCase : str = beta.log()
UpperCAmelCase : Tuple = (predicted_variance + 1) / 2
UpperCAmelCase : Dict = frac * max_log + (1 - frac) * min_log
return variance
def UpperCAmelCase_ ( self : Any , lowercase_ : torch.FloatTensor , lowercase_ : int , lowercase_ : torch.FloatTensor , lowercase_ : Optional[int] = None , lowercase_ : Any=None , lowercase_ : bool = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
UpperCAmelCase : List[Any] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
UpperCAmelCase : Union[str, Any] = torch.split(lowercase_ , sample.shape[1] , dim=1 )
else:
UpperCAmelCase : str = None
# 1. compute alphas, betas
if prev_timestep is None:
UpperCAmelCase : List[str] = t - 1
UpperCAmelCase : Any = self.alphas_cumprod[t]
UpperCAmelCase : Tuple = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase : str = 1 - alpha_prod_t
UpperCAmelCase : str = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase : List[str] = self.betas[t]
UpperCAmelCase : str = self.alphas[t]
else:
UpperCAmelCase : str = 1 - alpha_prod_t / alpha_prod_t_prev
UpperCAmelCase : Any = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase : str = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase : Optional[Any] = model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
' for the UnCLIPScheduler.' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase : int = torch.clamp(
lowercase_ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase : Optional[int] = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
UpperCAmelCase : List[Any] = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase : Dict = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCAmelCase : List[Any] = 0
if t > 0:
UpperCAmelCase : Tuple = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=lowercase_ , device=model_output.device )
UpperCAmelCase : List[str] = self._get_variance(
lowercase_ , predicted_variance=lowercase_ , prev_timestep=lowercase_ , )
if self.variance_type == "fixed_small_log":
UpperCAmelCase : str = variance
elif self.variance_type == "learned_range":
UpperCAmelCase : List[str] = (0.5 * variance).exp()
else:
raise ValueError(
f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
' for the UnCLIPScheduler.' )
UpperCAmelCase : List[str] = variance * variance_noise
UpperCAmelCase : int = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=lowercase_ , pred_original_sample=lowercase_ )
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : torch.FloatTensor , lowercase_ : torch.FloatTensor , lowercase_ : torch.IntTensor , ) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
UpperCAmelCase : List[Any] = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
UpperCAmelCase : List[Any] = timesteps.to(original_samples.device )
UpperCAmelCase : Tuple = alphas_cumprod[timesteps] ** 0.5
UpperCAmelCase : Optional[int] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase : Dict = sqrt_alpha_prod.unsqueeze(-1 )
UpperCAmelCase : Dict = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCAmelCase : Union[str, Any] = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase : int = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
UpperCAmelCase : List[Any] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 706 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
lowercase__ = "Create a default config file for Accelerate with only a few flags set."
def UpperCamelCase( UpperCAmelCase_="no" , UpperCAmelCase_ = default_json_config_file , UpperCAmelCase_ = False ):
UpperCAmelCase : Any = Path(UpperCAmelCase_ )
path.parent.mkdir(parents=UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
if path.exists():
print(
F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
UpperCAmelCase : Optional[int] = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
UpperCAmelCase : Dict = {
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
UpperCAmelCase : Dict = torch.cuda.device_count()
UpperCAmelCase : List[Any] = num_gpus
UpperCAmelCase : List[Any] = False
if num_gpus > 1:
UpperCAmelCase : Tuple = 'MULTI_GPU'
else:
UpperCAmelCase : Optional[Any] = 'NO'
elif is_xpu_available() and use_xpu:
UpperCAmelCase : Optional[int] = torch.xpu.device_count()
UpperCAmelCase : Optional[int] = num_xpus
UpperCAmelCase : Any = False
if num_xpus > 1:
UpperCAmelCase : Tuple = 'MULTI_XPU'
else:
UpperCAmelCase : str = 'NO'
elif is_npu_available():
UpperCAmelCase : Optional[int] = torch.npu.device_count()
UpperCAmelCase : str = num_npus
UpperCAmelCase : int = False
if num_npus > 1:
UpperCAmelCase : int = 'MULTI_NPU'
else:
UpperCAmelCase : List[str] = 'NO'
else:
UpperCAmelCase : str = 0
UpperCAmelCase : int = True
UpperCAmelCase : str = 1
UpperCAmelCase : str = 'NO'
UpperCAmelCase : Any = ClusterConfig(**UpperCAmelCase_ )
config.to_json_file(UpperCAmelCase_ )
return path
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = parser.add_parser('default' , parents=UpperCAmelCase_ , help=UpperCAmelCase_ , formatter_class=UpperCAmelCase_ )
parser.add_argument(
'--config_file' , default=UpperCAmelCase_ , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , dest='save_location' , )
parser.add_argument(
'--mixed_precision' , choices=['no', 'fp16', 'bf16'] , type=UpperCAmelCase_ , help='Whether or not to use mixed precision training. '
'Choose between FP16 and BF16 (bfloat16) training. '
'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' , default='no' , )
parser.set_defaults(func=UpperCAmelCase_ )
return parser
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F"""accelerate configuration saved at {config_file}""" )
| 695 | 0 |
'''simple docstring'''
import string
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Tuple = ''
for i in sequence:
UpperCAmelCase : Any = ord(UpperCAmelCase_ )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <= 1_22:
output += chr(2_19 - extract )
else:
output += i
return output
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = string.ascii_letters
UpperCAmelCase : Any = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(UpperCAmelCase_ )] if c in letters else c for c in sequence )
def UpperCamelCase( ):
from timeit import timeit
print('Running performance benchmarks...' )
UpperCAmelCase : Any = 'from string import printable ; from __main__ import atbash, atbash_slow'
print(F"""> atbash_slow(): {timeit("atbash_slow(printable)" , setup=UpperCAmelCase_ )} seconds""" )
print(F"""> atbash(): {timeit("atbash(printable)" , setup=UpperCAmelCase_ )} seconds""" )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f'''{example} encrypted in atbash: {atbash(example)}''')
benchmark()
| 707 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowercase__ = logging.get_logger(__name__)
class A_ ( _snake_case ):
'''simple docstring'''
def __init__( self : List[Any] , *lowercase_ : str , **lowercase_ : Union[str, Any] ) -> None:
warnings.warn(
'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use LayoutLMv2ImageProcessor instead.' , lowercase_ , )
super().__init__(*lowercase_ , **lowercase_ )
| 695 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = None , UpperCAmelCase_ = None , UpperCAmelCase_ = None , ):
if config_name_or_path is None:
UpperCAmelCase : Any = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'
if generator_tokenizer_name_or_path is None:
UpperCAmelCase : Tuple = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
UpperCAmelCase : Union[str, Any] = question_encoder_name_or_path
UpperCAmelCase : Dict = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration
# Save model.
UpperCAmelCase : List[str] = RagConfig.from_pretrained(UpperCAmelCase_ )
UpperCAmelCase : Dict = AutoConfig.from_pretrained(UpperCAmelCase_ )
UpperCAmelCase : Tuple = AutoConfig.from_pretrained(UpperCAmelCase_ )
UpperCAmelCase : Dict = gen_config
UpperCAmelCase : Any = question_encoder_config
UpperCAmelCase : str = model_class.from_pretrained_question_encoder_generator(
UpperCAmelCase_ , UpperCAmelCase_ , config=UpperCAmelCase_ )
rag_model.save_pretrained(UpperCAmelCase_ )
# Sanity check.
model_class.from_pretrained(UpperCAmelCase_ )
# Save tokenizers.
UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(UpperCAmelCase_ )
gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/' )
UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained(UpperCAmelCase_ )
question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/' )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--model_type",
choices=["rag_sequence", "rag_token"],
required=True,
type=str,
help="RAG model type: rag_sequence, rag_token",
)
parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
parser.add_argument(
"--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
)
parser.add_argument(
"--generator_tokenizer_name_or_path",
type=str,
help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
)
parser.add_argument(
"--question_encoder_tokenizer_name_or_path",
type=str,
help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
)
parser.add_argument(
"--config_name_or_path",
type=str,
help=(
"Identifier of the model config to use, if not provided, resolves to a base config for a given"
" ``model_type``"
),
)
lowercase__ = parser.parse_args()
lowercase__ = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 708 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
lowercase__ = logging.getLogger(__name__)
@dataclass(frozen=_snake_case )
class A_ :
'''simple docstring'''
UpperCAmelCase_ : str
UpperCAmelCase_ : str
UpperCAmelCase_ : Optional[str] = None
UpperCAmelCase_ : Optional[str] = None
UpperCAmelCase_ : Optional[str] = None
@dataclass(frozen=_snake_case )
class A_ :
'''simple docstring'''
UpperCAmelCase_ : List[int]
UpperCAmelCase_ : Optional[List[int]] = None
UpperCAmelCase_ : Optional[List[int]] = None
UpperCAmelCase_ : Optional[Union[int, float]] = None
UpperCAmelCase_ : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : List[InputFeatures]
def __init__( self : List[str] , lowercase_ : str , lowercase_ : PreTrainedTokenizer , lowercase_ : str , lowercase_ : Optional[int] = None , lowercase_ : List[str]=False , lowercase_ : bool = False , ) -> Optional[Any]:
UpperCAmelCase : Dict = hans_processors[task]()
UpperCAmelCase : List[Any] = os.path.join(
lowercase_ , 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train' , tokenizer.__class__.__name__ , str(lowercase_ ) , lowercase_ , ) , )
UpperCAmelCase : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCAmelCase , UpperCAmelCase : Tuple = label_list[2], label_list[1]
UpperCAmelCase : Optional[int] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
UpperCAmelCase : int = cached_features_file + '.lock'
with FileLock(lowercase_ ):
if os.path.exists(lowercase_ ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
UpperCAmelCase : Tuple = torch.load(lowercase_ )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
UpperCAmelCase : int = (
processor.get_dev_examples(lowercase_ ) if evaluate else processor.get_train_examples(lowercase_ )
)
logger.info('Training examples: %s' , len(lowercase_ ) )
UpperCAmelCase : Dict = hans_convert_examples_to_features(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
logger.info('Saving features into cached file %s' , lowercase_ )
torch.save(self.features , lowercase_ )
def __len__( self : Union[str, Any] ) -> str:
return len(self.features )
def __getitem__( self : Dict , lowercase_ : Dict ) -> InputFeatures:
return self.features[i]
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
return self.label_list
if is_tf_available():
import tensorflow as tf
class A_ :
'''simple docstring'''
UpperCAmelCase_ : List[InputFeatures]
def __init__( self : Tuple , lowercase_ : str , lowercase_ : PreTrainedTokenizer , lowercase_ : str , lowercase_ : Optional[int] = 128 , lowercase_ : int=False , lowercase_ : bool = False , ) -> Union[str, Any]:
UpperCAmelCase : int = hans_processors[task]()
UpperCAmelCase : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
UpperCAmelCase , UpperCAmelCase : Tuple = label_list[2], label_list[1]
UpperCAmelCase : Any = label_list
UpperCAmelCase : str = processor.get_dev_examples(lowercase_ ) if evaluate else processor.get_train_examples(lowercase_ )
UpperCAmelCase : int = hans_convert_examples_to_features(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='convert examples to features' ):
if ex_index % 10_000 == 0:
logger.info('Writing example %d of %d' % (ex_index, len(lowercase_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
UpperCAmelCase : Optional[Any] = tf.data.Dataset.from_generator(
lowercase_ , (
{
'example_id': tf.intaa,
'input_ids': tf.intaa,
'attention_mask': tf.intaa,
'token_type_ids': tf.intaa,
},
tf.intaa,
) , (
{
'example_id': tf.TensorShape([] ),
'input_ids': tf.TensorShape([None, None] ),
'attention_mask': tf.TensorShape([None, None] ),
'token_type_ids': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
return self.dataset
def __len__( self : Tuple ) -> Optional[Any]:
return len(self.features )
def __getitem__( self : List[Any] , lowercase_ : Union[str, Any] ) -> InputFeatures:
return self.features[i]
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
return self.label_list
class A_ ( _snake_case ):
'''simple docstring'''
def UpperCAmelCase_ ( self : int , lowercase_ : Optional[int] ) -> Any:
return self._create_examples(self._read_tsv(os.path.join(lowercase_ , 'heuristics_train_set.txt' ) ) , 'train' )
def UpperCAmelCase_ ( self : Optional[int] , lowercase_ : Dict ) -> List[str]:
return self._create_examples(self._read_tsv(os.path.join(lowercase_ , 'heuristics_evaluation_set.txt' ) ) , 'dev' )
def UpperCAmelCase_ ( self : str ) -> Optional[int]:
return ["contradiction", "entailment", "neutral"]
def UpperCAmelCase_ ( self : Optional[int] , lowercase_ : Tuple , lowercase_ : str ) -> Dict:
UpperCAmelCase : Union[str, Any] = []
for i, line in enumerate(lowercase_ ):
if i == 0:
continue
UpperCAmelCase : Tuple = '%s-%s' % (set_type, line[0])
UpperCAmelCase : Tuple = line[5]
UpperCAmelCase : Dict = line[6]
UpperCAmelCase : Optional[Any] = line[7][2:] if line[7].startswith('ex' ) else line[7]
UpperCAmelCase : Optional[Any] = line[0]
examples.append(InputExample(guid=lowercase_ , text_a=lowercase_ , text_b=lowercase_ , label=lowercase_ , pairID=lowercase_ ) )
return examples
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
UpperCAmelCase : List[str] = {label: i for i, label in enumerate(UpperCAmelCase_ )}
UpperCAmelCase : Optional[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(UpperCAmelCase_ ) , desc='convert examples to features' ):
if ex_index % 1_00_00 == 0:
logger.info('Writing example %d' % (ex_index) )
UpperCAmelCase : int = tokenizer(
example.text_a , example.text_b , add_special_tokens=UpperCAmelCase_ , max_length=UpperCAmelCase_ , padding='max_length' , truncation=UpperCAmelCase_ , return_overflowing_tokens=UpperCAmelCase_ , )
UpperCAmelCase : List[str] = label_map[example.label] if example.label in label_map else 0
UpperCAmelCase : Any = int(example.pairID )
features.append(InputFeatures(**UpperCAmelCase_ , label=UpperCAmelCase_ , pairID=UpperCAmelCase_ ) )
for i, example in enumerate(examples[:5] ):
logger.info('*** Example ***' )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
lowercase__ = {
"hans": 3,
}
lowercase__ = {
"hans": HansProcessor,
}
| 695 | 0 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
UpperCAmelCase : List[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCAmelCase : Optional[int] = 1
if upper_limit > 0:
UpperCAmelCase : int = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(UpperCAmelCase_ ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
print("\n*** Enter -1 at any time to quit ***")
print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
try:
while True:
lowercase__ = int(input().strip())
if N < 0:
print("\n********* Goodbye!! ************")
break
else:
print(f'''The Catalan numbers from 0 through {N} are:''')
print(catalan_numbers(N))
print("Try another upper limit for the sequence: ", end="")
except (NameError, ValueError):
print("\n********* Invalid input, goodbye! ************\n")
import doctest
doctest.testmod()
| 709 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ = 10_00 ):
UpperCAmelCase , UpperCAmelCase : Any = 1, 1
UpperCAmelCase : Any = []
for i in range(1 , n + 1 ):
UpperCAmelCase : Tuple = prev_numerator + 2 * prev_denominator
UpperCAmelCase : Any = prev_numerator + prev_denominator
if len(str(UpperCAmelCase_ ) ) > len(str(UpperCAmelCase_ ) ):
result.append(UpperCAmelCase_ )
UpperCAmelCase : Dict = numerator
UpperCAmelCase : Dict = denominator
return len(UpperCAmelCase_ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 695 | 0 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : List[str] , lowercase_ : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : int = 3
UpperCAmelCase : Optional[int] = 250
UpperCAmelCase : Any = ids_tensor((batch_size, length) , lowercase_ )
UpperCAmelCase : Optional[int] = torch.ones((batch_size, length) , device=lowercase_ , dtype=torch.float ) / length
return input_ids, scores
def UpperCAmelCase_ ( self : List[str] ) -> Optional[int]:
UpperCAmelCase : Any = self._get_tensors(5 )
UpperCAmelCase : Tuple = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase : Union[str, Any] = self._get_tensors(9 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase : Any = self._get_tensors(10 )
self.assertTrue(criteria(lowercase_ , lowercase_ ) )
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = MaxLengthCriteria(max_length=10 )
UpperCAmelCase : Optional[int] = self._get_tensors(5 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase : Any = self._get_tensors(9 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase : Union[str, Any] = self._get_tensors(10 )
self.assertTrue(criteria(lowercase_ , lowercase_ ) )
def UpperCAmelCase_ ( self : Dict ) -> str:
UpperCAmelCase : List[str] = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
UpperCAmelCase : List[Any] = self._get_tensors(5 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase : List[Any] = self._get_tensors(9 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase : int = self._get_tensors(10 )
self.assertTrue(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase : List[str] = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 10 )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
UpperCAmelCase : Dict = self._get_tensors(5 )
UpperCAmelCase : Union[str, Any] = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(lowercase_ , lowercase_ ) )
UpperCAmelCase : int = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(lowercase_ , lowercase_ ) )
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 10 )
with self.assertWarns(lowercase_ ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 11 )
UpperCAmelCase : Optional[Any] = validate_stopping_criteria(StoppingCriteriaList() , 11 )
self.assertEqual(len(lowercase_ ) , 1 )
| 710 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ = 10_00 ):
UpperCAmelCase : List[Any] = 2**power
UpperCAmelCase : List[Any] = 0
while n:
UpperCAmelCase , UpperCAmelCase : Optional[Any] = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 695 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
lowercase__ = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"
lowercase__ = "\nArgs:\n predictions (`list` of `int`): Predicted class labels, as returned by a model.\n references (`list` of `int`): Ground truth labels.\n return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n Example 1-A simple example using only predictions and references.\n >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n >>> print(round(results['pearsonr'], 2))\n -0.74\n\n Example 2-The same as Example 1, but that also returns the `p-value`.\n >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n >>> print(sorted(list(results.keys())))\n ['p-value', 'pearsonr']\n >>> print(round(results['pearsonr'], 2))\n -0.74\n >>> print(round(results['p-value'], 2))\n 0.15\n"
lowercase__ = "\n@article{2020SciPy-NMeth,\nauthor = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Antonio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\njournal = {Nature Methods},\nyear = {2020},\nvolume = {17},\npages = {261--272},\nadsurl = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'] , )
def UpperCAmelCase_ ( self : str , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : List[str]=False ) -> Optional[int]:
if return_pvalue:
UpperCAmelCase : List[str] = pearsonr(lowercase_ , lowercase_ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(lowercase_ , lowercase_ )[0] )}
| 711 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : str = """blip_2_vision_model"""
def __init__( self : List[str] , lowercase_ : int=1_408 , lowercase_ : Tuple=6_144 , lowercase_ : Dict=39 , lowercase_ : Optional[int]=16 , lowercase_ : str=224 , lowercase_ : Any=14 , lowercase_ : int="gelu" , lowercase_ : int=0.0_0001 , lowercase_ : Optional[int]=0.0 , lowercase_ : Dict=1E-10 , lowercase_ : List[str]=True , **lowercase_ : Optional[Any] , ) -> Union[str, Any]:
super().__init__(**lowercase_ )
UpperCAmelCase : Optional[int] = hidden_size
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : List[Any] = num_hidden_layers
UpperCAmelCase : Any = num_attention_heads
UpperCAmelCase : str = patch_size
UpperCAmelCase : Union[str, Any] = image_size
UpperCAmelCase : List[Any] = initializer_range
UpperCAmelCase : str = attention_dropout
UpperCAmelCase : str = layer_norm_eps
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : str = qkv_bias
@classmethod
def UpperCAmelCase_ ( cls : List[str] , lowercase_ : Union[str, os.PathLike] , **lowercase_ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowercase_ )
UpperCAmelCase , UpperCAmelCase : Optional[Any] = cls.get_config_dict(lowercase_ , **lowercase_ )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
UpperCAmelCase : Dict = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowercase_ , **lowercase_ )
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = """blip_2_qformer"""
def __init__( self : Tuple , lowercase_ : Union[str, Any]=30_522 , lowercase_ : int=768 , lowercase_ : Dict=12 , lowercase_ : Dict=12 , lowercase_ : int=3_072 , lowercase_ : Optional[Any]="gelu" , lowercase_ : Union[str, Any]=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : Tuple=512 , lowercase_ : Optional[int]=0.02 , lowercase_ : str=1E-12 , lowercase_ : str=0 , lowercase_ : Union[str, Any]="absolute" , lowercase_ : Optional[int]=2 , lowercase_ : str=1_408 , **lowercase_ : Optional[Any] , ) -> Optional[Any]:
super().__init__(pad_token_id=lowercase_ , **lowercase_ )
UpperCAmelCase : int = vocab_size
UpperCAmelCase : Union[str, Any] = hidden_size
UpperCAmelCase : List[str] = num_hidden_layers
UpperCAmelCase : str = num_attention_heads
UpperCAmelCase : List[Any] = hidden_act
UpperCAmelCase : Union[str, Any] = intermediate_size
UpperCAmelCase : Optional[int] = hidden_dropout_prob
UpperCAmelCase : List[Any] = attention_probs_dropout_prob
UpperCAmelCase : Tuple = max_position_embeddings
UpperCAmelCase : Optional[Any] = initializer_range
UpperCAmelCase : Any = layer_norm_eps
UpperCAmelCase : Dict = position_embedding_type
UpperCAmelCase : Any = cross_attention_frequency
UpperCAmelCase : Any = encoder_hidden_size
@classmethod
def UpperCAmelCase_ ( cls : List[Any] , lowercase_ : Union[str, os.PathLike] , **lowercase_ : List[str] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowercase_ )
UpperCAmelCase , UpperCAmelCase : List[str] = cls.get_config_dict(lowercase_ , **lowercase_ )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
UpperCAmelCase : Dict = config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowercase_ , **lowercase_ )
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = """blip-2"""
UpperCAmelCase_ : Any = True
def __init__( self : Union[str, Any] , lowercase_ : List[Any]=None , lowercase_ : Union[str, Any]=None , lowercase_ : Dict=None , lowercase_ : Dict=32 , **lowercase_ : Union[str, Any] ) -> Any:
super().__init__(**lowercase_ )
if vision_config is None:
UpperCAmelCase : Union[str, Any] = {}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
UpperCAmelCase : int = {}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
UpperCAmelCase : Dict = {}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
UpperCAmelCase : str = BlipaVisionConfig(**lowercase_ )
UpperCAmelCase : str = BlipaQFormerConfig(**lowercase_ )
UpperCAmelCase : Union[str, Any] = text_config['model_type'] if 'model_type' in text_config else 'opt'
UpperCAmelCase : int = CONFIG_MAPPING[text_model_type](**lowercase_ )
UpperCAmelCase : Optional[int] = self.text_config.tie_word_embeddings
UpperCAmelCase : Dict = self.text_config.is_encoder_decoder
UpperCAmelCase : Tuple = num_query_tokens
UpperCAmelCase : Tuple = self.vision_config.hidden_size
UpperCAmelCase : List[Any] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
UpperCAmelCase : Union[str, Any] = 1.0
UpperCAmelCase : Union[str, Any] = 0.02
@classmethod
def UpperCAmelCase_ ( cls : Dict , lowercase_ : BlipaVisionConfig , lowercase_ : BlipaQFormerConfig , lowercase_ : PretrainedConfig , **lowercase_ : List[Any] , ) -> Tuple:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **lowercase_ , )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[int]:
UpperCAmelCase : Dict = copy.deepcopy(self.__dict__ )
UpperCAmelCase : Optional[int] = self.vision_config.to_dict()
UpperCAmelCase : Optional[int] = self.qformer_config.to_dict()
UpperCAmelCase : List[str] = self.text_config.to_dict()
UpperCAmelCase : str = self.__class__.model_type
return output
| 695 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ = "▁"
lowercase__ = {"vocab_file": "spiece.model"}
lowercase__ = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}
lowercase__ = {
"google/pegasus-xsum": 512,
}
lowercase__ = logging.get_logger(__name__)
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = VOCAB_FILES_NAMES
UpperCAmelCase_ : int = VOCAB_FILES_NAMES
UpperCAmelCase_ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ : Dict = ["""input_ids""", """attention_mask"""]
def __init__( self : Any , lowercase_ : int , lowercase_ : Optional[Any]="<pad>" , lowercase_ : Dict="</s>" , lowercase_ : int="<unk>" , lowercase_ : Optional[int]="<mask_2>" , lowercase_ : str="<mask_1>" , lowercase_ : List[str]=None , lowercase_ : List[Any]=103 , lowercase_ : Optional[Dict[str, Any]] = None , **lowercase_ : Union[str, Any] , ) -> None:
UpperCAmelCase : Any = offset
if additional_special_tokens is not None:
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError(
f"""additional_special_tokens should be of type {type(lowercase_ )}, but is"""
f""" {type(lowercase_ )}""" )
UpperCAmelCase : Optional[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f"""<unk_{i}>""" for i in range(len(lowercase_ ) , self.offset - 1 )
]
if len(set(lowercase_ ) ) != len(lowercase_ ):
raise ValueError(
'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
UpperCAmelCase : Optional[Any] = additional_special_tokens_extended
else:
UpperCAmelCase : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f"""<unk_{i}>""" for i in range(2 , self.offset )]
UpperCAmelCase : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowercase_ , unk_token=lowercase_ , mask_token=lowercase_ , pad_token=lowercase_ , mask_token_sent=lowercase_ , offset=lowercase_ , additional_special_tokens=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
UpperCAmelCase : Dict = mask_token_sent
UpperCAmelCase : Any = vocab_file
UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase_ )
# add special tokens to encoder dict
UpperCAmelCase : Dict[int, str] = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
UpperCAmelCase : Dict[str, int] = {v: k for k, v in self.encoder.items()}
@property
def UpperCAmelCase_ ( self : Any ) -> int:
return len(self.sp_model ) + self.offset
def UpperCAmelCase_ ( self : str ) -> Dict[str, int]:
UpperCAmelCase : List[str] = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ) -> Union[str, Any]:
UpperCAmelCase : Tuple = self.__dict__.copy()
UpperCAmelCase : Optional[int] = None
return state
def __setstate__( self : List[str] , lowercase_ : Union[str, Any] ) -> Optional[Any]:
UpperCAmelCase : Tuple = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCAmelCase : Optional[Any] = {}
UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : str ) -> List[str]:
return self.sp_model.encode(lowercase_ , out_type=lowercase_ )
def UpperCAmelCase_ ( self : Any , lowercase_ : str ) -> int:
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
UpperCAmelCase : Union[str, Any] = self.sp_model.piece_to_id(lowercase_ )
return sp_id + self.offset
def UpperCAmelCase_ ( self : List[str] , lowercase_ : int ) -> str:
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
UpperCAmelCase : int = self.sp_model.IdToPiece(index - self.offset )
return token
def UpperCAmelCase_ ( self : int , lowercase_ : Optional[Any] ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = []
UpperCAmelCase : str = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowercase_ ) + token
UpperCAmelCase : Optional[int] = []
else:
current_sub_tokens.append(lowercase_ )
out_string += self.sp_model.decode(lowercase_ )
return out_string.strip()
def UpperCAmelCase_ ( self : int , lowercase_ : List[str]=False ) -> int:
return 1
def UpperCAmelCase_ ( self : Tuple , lowercase_ : List[Any] ) -> Any:
UpperCAmelCase : List[Any] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def UpperCAmelCase_ ( self : Dict , lowercase_ : List , lowercase_ : Optional[List] = None , lowercase_ : bool = False ) -> List[int]:
if already_has_special_tokens:
return self._special_token_mask(lowercase_ )
elif token_ids_a is None:
return self._special_token_mask(lowercase_ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def UpperCAmelCase_ ( self : Dict , lowercase_ : Tuple , lowercase_ : Any=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self : List[str] , lowercase_ : str , lowercase_ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowercase_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase : Union[str, Any] = os.path.join(
lowercase_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ , 'wb' ) as fi:
UpperCAmelCase : List[Any] = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
| 712 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowercase__ = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
lowercase__ = json.load(f)
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Dict , lowercase_ : Dict ) -> Tuple:
return FSMTTokenizer.from_pretrained(lowercase_ )
def UpperCAmelCase_ ( self : Optional[int] , lowercase_ : Dict ) -> Tuple:
UpperCAmelCase : Optional[Any] = FSMTForConditionalGeneration.from_pretrained(lowercase_ ).to(lowercase_ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['en-ru', 26.0],
['ru-en', 22.0],
['en-de', 22.0],
['de-en', 29.0],
] )
@slow
def UpperCAmelCase_ ( self : List[str] , lowercase_ : int , lowercase_ : Any ) -> Optional[int]:
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
UpperCAmelCase : List[str] = f"""facebook/wmt19-{pair}"""
UpperCAmelCase : Optional[int] = self.get_tokenizer(lowercase_ )
UpperCAmelCase : int = self.get_model(lowercase_ )
UpperCAmelCase : List[Any] = bleu_data[pair]['src']
UpperCAmelCase : Optional[int] = bleu_data[pair]['tgt']
UpperCAmelCase : Any = tokenizer(lowercase_ , return_tensors='pt' , truncation=lowercase_ , padding='longest' ).to(lowercase_ )
UpperCAmelCase : List[Any] = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
UpperCAmelCase : List[Any] = tokenizer.batch_decode(
lowercase_ , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ )
UpperCAmelCase : Any = calculate_bleu(lowercase_ , lowercase_ )
print(lowercase_ )
self.assertGreaterEqual(scores['bleu'] , lowercase_ )
| 695 | 0 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowercase__ = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Dict = test_results.split(' ' )
UpperCAmelCase : Optional[int] = 0
UpperCAmelCase : Tuple = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
UpperCAmelCase : Union[str, Any] = expressions[-2] if '=' in expressions[-1] else expressions[-1]
for i, expression in enumerate(UpperCAmelCase_ ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Tuple = {}
UpperCAmelCase : Any = None
UpperCAmelCase : Dict = False
for line in failures_short_lines.split('\n' ):
if re.search(R'_ \[doctest\]' , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = True
UpperCAmelCase : List[Any] = line.split(' ' )[2]
elif in_error and not line.split(' ' )[0].isdigit():
UpperCAmelCase : Dict = line
UpperCAmelCase : Optional[Any] = False
return failures
class A_ :
'''simple docstring'''
def __init__( self : List[Any] , lowercase_ : str , lowercase_ : Dict ) -> Tuple:
UpperCAmelCase : Tuple = title
UpperCAmelCase : Union[str, Any] = doc_test_results['time_spent'].split(',' )[0]
UpperCAmelCase : str = doc_test_results['success']
UpperCAmelCase : Any = doc_test_results['failures']
UpperCAmelCase : int = self.n_success + self.n_failures
# Failures and success of the modeling tests
UpperCAmelCase : int = doc_test_results
@property
def UpperCAmelCase_ ( self : int ) -> str:
UpperCAmelCase : int = [self._time_spent]
UpperCAmelCase : Optional[int] = 0
for time in time_spent:
UpperCAmelCase : Optional[Any] = time.split(':' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(lowercase_ ) == 1:
UpperCAmelCase : str = [0, 0, time_parts[0]]
UpperCAmelCase : Union[str, Any] = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3_600 + minutes * 60 + seconds
UpperCAmelCase : Tuple = total_secs // 3_600, (total_secs % 3_600) // 60, total_secs % 60
return f"""{int(lowercase_ )}h{int(lowercase_ )}m{int(lowercase_ )}s"""
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Dict:
return {
"type": "section",
"text": {
"type": "plain_text",
"text": f"""🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.""",
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Dict:
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
f"""There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"""
f""" {self.time}."""
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
@property
def UpperCAmelCase_ ( self : str ) -> Dict:
UpperCAmelCase : int = 40
UpperCAmelCase : Union[str, Any] = {k: v['failed'] for k, v in doc_test_results.items() if isinstance(lowercase_ , lowercase_ )}
UpperCAmelCase : Union[str, Any] = ''
for category, failures in category_failures.items():
if len(lowercase_ ) == 0:
continue
if report != "":
report += "\n\n"
report += f"""*{category} failures*:""".ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(lowercase_ )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f"""The following examples had failures:\n\n\n{report}\n""",
},
}
@property
def UpperCAmelCase_ ( self : List[str] ) -> str:
UpperCAmelCase : Optional[int] = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(lowercase_ )
@staticmethod
def UpperCAmelCase_ ( ) -> Dict:
UpperCAmelCase : Optional[int] = [
{
'type': 'section',
'text': {
'type': 'plain_text',
'text': 'There was an issue running the tests.',
},
'accessory': {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
'url': f"""https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
]
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(lowercase_ )} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , text='There was an issue running the tests.' , blocks=lowercase_ , )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(self.payload )} ) )
UpperCAmelCase : Dict = f"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else 'All tests passed.'
UpperCAmelCase : int = client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , blocks=self.payload , text=lowercase_ , )
def UpperCAmelCase_ ( self : List[str] , lowercase_ : int , lowercase_ : List[str] , lowercase_ : List[Any] , lowercase_ : int ) -> str:
UpperCAmelCase : Tuple = ''
for key, value in failures.items():
UpperCAmelCase : int = value[:200] + ' [Truncated]' if len(lowercase_ ) > 250 else value
failures_text += f"""*{key}*\n_{value}_\n\n"""
UpperCAmelCase : Any = job_name
UpperCAmelCase : Any = {'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}
if job_link is not None:
UpperCAmelCase : Optional[Any] = {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
'url': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
if self.thread_ts is None:
raise ValueError('Can only post reply if a post has been made.' )
UpperCAmelCase : Union[str, Any] = self.doc_test_results.pop('job_link' )
self.doc_test_results.pop('failures' )
self.doc_test_results.pop('success' )
self.doc_test_results.pop('time_spent' )
UpperCAmelCase : Optional[Any] = sorted(self.doc_test_results.items() , key=lambda lowercase_ : t[0] )
for job, job_result in sorted_dict:
if len(job_result['failures'] ):
UpperCAmelCase : Dict = f"""*Num failures* :{len(job_result["failed"] )} \n"""
UpperCAmelCase : Optional[Any] = job_result['failures']
UpperCAmelCase : Any = self.get_reply_blocks(lowercase_ , lowercase_ , lowercase_ , text=lowercase_ )
print('Sending the following reply' )
print(json.dumps({'blocks': blocks} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , text=f"""Results for {job}""" , blocks=lowercase_ , thread_ts=self.thread_ts['ts'] , )
time.sleep(1 )
def UpperCamelCase( ):
UpperCAmelCase : int = os.environ['GITHUB_RUN_ID']
UpperCAmelCase : Optional[int] = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
UpperCAmelCase : Dict = requests.get(UpperCAmelCase_ ).json()
UpperCAmelCase : Dict = {}
try:
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
UpperCAmelCase : Union[str, Any] = math.ceil((result['total_count'] - 1_00) / 1_00 )
for i in range(UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = requests.get(url + F"""&page={i + 2}""" ).json()
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
return jobs
except Exception as e:
print('Unknown error, could not fetch links.' , UpperCAmelCase_ )
return {}
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : List[str] = {}
if os.path.exists(UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = os.listdir(UpperCAmelCase_ )
for file in files:
try:
with open(os.path.join(UpperCAmelCase_ , UpperCAmelCase_ ) , encoding='utf-8' ) as f:
UpperCAmelCase : Optional[Any] = f.read()
except UnicodeDecodeError as e:
raise ValueError(F"""Could not open {os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )}.""" ) from e
return _artifact
def UpperCamelCase( ):
class A_ :
'''simple docstring'''
def __init__( self : List[str] , lowercase_ : str ) -> Dict:
UpperCAmelCase : List[Any] = name
UpperCAmelCase : Tuple = []
def __str__( self : Any ) -> Dict:
return self.name
def UpperCAmelCase_ ( self : Union[str, Any] , lowercase_ : str ) -> List[Any]:
self.paths.append({'name': self.name, 'path': path} )
UpperCAmelCase : Dict[str, Artifact] = {}
UpperCAmelCase : Union[str, Any] = filter(os.path.isdir , os.listdir() )
for directory in directories:
UpperCAmelCase : Tuple = directory
if artifact_name not in _available_artifacts:
UpperCAmelCase : Optional[int] = Artifact(UpperCAmelCase_ )
_available_artifacts[artifact_name].add_path(UpperCAmelCase_ )
return _available_artifacts
if __name__ == "__main__":
lowercase__ = get_job_links()
lowercase__ = retrieve_available_artifacts()
lowercase__ = collections.OrderedDict(
[
("*.py", "API Examples"),
("*.md", "MD Examples"),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
lowercase__ = {
v: {
"failed": [],
"failures": {},
}
for v in docs.values()
}
# Link to the GitHub Action job
lowercase__ = github_actions_job_links.get("run_doctests")
lowercase__ = available_artifacts["doc_tests_gpu_test_reports"].paths[0]
lowercase__ = retrieve_artifact(artifact_path["name"])
if "stats" in artifact:
lowercase__ , lowercase__ , lowercase__ = handle_test_results(artifact["stats"])
lowercase__ = failed
lowercase__ = success
lowercase__ = time_spent[1:-1] + ", "
lowercase__ = extract_first_line_failure(artifact["failures_short"])
for line in artifact["summary_short"].split("\n"):
if re.search("FAILED", line):
lowercase__ = line.replace("FAILED ", "")
lowercase__ = line.split()[0].replace("\n", "")
if "::" in line:
lowercase__ , lowercase__ = line.split("::")
else:
lowercase__ , lowercase__ = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
lowercase__ = docs[file_regex]
doc_test_results[category]["failed"].append(test)
lowercase__ = all_failures[test] if test in all_failures else "N/A"
lowercase__ = failure
break
lowercase__ = Message("🤗 Results of the doc tests.", doc_test_results)
message.post()
message.post_reply()
| 713 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"google/pix2struct-textcaps-base": (
"https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
),
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = """pix2struct_text_model"""
UpperCAmelCase_ : Union[str, Any] = ["""past_key_values"""]
UpperCAmelCase_ : Optional[int] = {
"""hidden_size""": """hidden_size""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : int , lowercase_ : str=50_244 , lowercase_ : Tuple=768 , lowercase_ : List[Any]=64 , lowercase_ : List[Any]=2_048 , lowercase_ : Optional[Any]=12 , lowercase_ : Union[str, Any]=12 , lowercase_ : Union[str, Any]=32 , lowercase_ : List[str]=128 , lowercase_ : List[Any]=0.1 , lowercase_ : List[str]=1E-6 , lowercase_ : Union[str, Any]=1.0 , lowercase_ : Dict="gelu_new" , lowercase_ : Any=0 , lowercase_ : Any=False , lowercase_ : List[Any]=0 , lowercase_ : Tuple=1 , lowercase_ : List[str]=False , lowercase_ : List[Any]=True , **lowercase_ : Union[str, Any] , ) -> Dict:
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : int = hidden_size
UpperCAmelCase : List[Any] = d_kv
UpperCAmelCase : Any = d_ff
UpperCAmelCase : List[str] = num_layers
UpperCAmelCase : str = num_heads
UpperCAmelCase : List[Any] = relative_attention_num_buckets
UpperCAmelCase : Tuple = relative_attention_max_distance
UpperCAmelCase : str = dropout_rate
UpperCAmelCase : Optional[int] = layer_norm_epsilon
UpperCAmelCase : int = initializer_factor
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : List[Any] = eos_token_id
UpperCAmelCase : Union[str, Any] = decoder_start_token_id
# for backwards compatibility
UpperCAmelCase : List[str] = dense_act_fn
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , decoder_start_token_id=lowercase_ , tie_word_embeddings=lowercase_ , is_decoder=lowercase_ , **lowercase_ , )
@classmethod
def UpperCAmelCase_ ( cls : Optional[Any] , lowercase_ : Union[str, os.PathLike] , **lowercase_ : List[str] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowercase_ )
UpperCAmelCase , UpperCAmelCase : str = cls.get_config_dict(lowercase_ , **lowercase_ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type' ) == "pix2struct":
UpperCAmelCase : Any = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowercase_ , **lowercase_ )
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : int = """pix2struct_vision_model"""
def __init__( self : str , lowercase_ : Any=768 , lowercase_ : Union[str, Any]=768 , lowercase_ : Union[str, Any]=2_048 , lowercase_ : Tuple=64 , lowercase_ : Dict=12 , lowercase_ : Optional[int]=12 , lowercase_ : int="gelu_new" , lowercase_ : List[Any]=1E-6 , lowercase_ : Optional[int]=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : str=1E-10 , lowercase_ : Dict=1.0 , lowercase_ : int=4_096 , lowercase_ : Tuple=32 , lowercase_ : Any=128 , **lowercase_ : Any , ) -> Tuple:
super().__init__(**lowercase_ )
UpperCAmelCase : Any = hidden_size
UpperCAmelCase : Any = patch_embed_hidden_size
UpperCAmelCase : Optional[int] = d_ff
UpperCAmelCase : Dict = dropout_rate
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : List[Any] = num_attention_heads
UpperCAmelCase : List[str] = initializer_range
UpperCAmelCase : str = initializer_factor
UpperCAmelCase : str = attention_dropout
UpperCAmelCase : str = layer_norm_eps
UpperCAmelCase : Union[str, Any] = dense_act_fn
UpperCAmelCase : Dict = seq_len
UpperCAmelCase : Optional[int] = relative_attention_num_buckets
UpperCAmelCase : Union[str, Any] = relative_attention_max_distance
UpperCAmelCase : str = d_kv
@classmethod
def UpperCAmelCase_ ( cls : Optional[Any] , lowercase_ : Union[str, os.PathLike] , **lowercase_ : Any ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowercase_ )
UpperCAmelCase , UpperCAmelCase : Tuple = cls.get_config_dict(lowercase_ , **lowercase_ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type' ) == "pix2struct":
UpperCAmelCase : List[str] = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowercase_ , **lowercase_ )
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = """pix2struct"""
UpperCAmelCase_ : Dict = True
def __init__( self : Union[str, Any] , lowercase_ : int=None , lowercase_ : int=None , lowercase_ : Optional[Any]=1.0 , lowercase_ : List[str]=0.02 , lowercase_ : str=False , lowercase_ : Union[str, Any]=False , lowercase_ : Tuple=True , **lowercase_ : Optional[Any] , ) -> str:
super().__init__(tie_word_embeddings=lowercase_ , is_encoder_decoder=lowercase_ , **lowercase_ )
if text_config is None:
UpperCAmelCase : Optional[int] = {}
logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.' )
if vision_config is None:
UpperCAmelCase : List[str] = {}
logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.' )
UpperCAmelCase : Optional[Any] = PixaStructTextConfig(**lowercase_ )
UpperCAmelCase : Union[str, Any] = PixaStructVisionConfig(**lowercase_ )
UpperCAmelCase : Optional[Any] = self.text_config.decoder_start_token_id
UpperCAmelCase : str = self.text_config.pad_token_id
UpperCAmelCase : Optional[int] = self.text_config.eos_token_id
UpperCAmelCase : Union[str, Any] = initializer_factor
UpperCAmelCase : List[str] = initializer_range
UpperCAmelCase : int = self.initializer_range
UpperCAmelCase : int = self.initializer_range
UpperCAmelCase : str = is_vqa
@classmethod
def UpperCAmelCase_ ( cls : Tuple , lowercase_ : PixaStructTextConfig , lowercase_ : PixaStructVisionConfig , **lowercase_ : str ) -> str:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **lowercase_ )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
UpperCAmelCase : List[Any] = copy.deepcopy(self.__dict__ )
UpperCAmelCase : Optional[int] = self.text_config.to_dict()
UpperCAmelCase : Dict = self.vision_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
| 695 | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase__ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def UpperCamelCase( UpperCAmelCase_ ) -> List[Any]:
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
UpperCAmelCase : List[Any] = list(s_dict.keys() )
for key in keys:
UpperCAmelCase : List[str] = R'.*/layers_(\d+)'
UpperCAmelCase : List[Any] = key
if re.match(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = re.sub(R'layers_(\d+)' , R'block/\1/layer' , UpperCAmelCase_ )
UpperCAmelCase : List[Any] = R'(encoder|decoder)\/'
if re.match(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Dict = re.match(UpperCAmelCase_ , UpperCAmelCase_ ).groups()
if groups[0] == "encoder":
UpperCAmelCase : Optional[Any] = re.sub(R'/mlp/' , R'/1/mlp/' , UpperCAmelCase_ )
UpperCAmelCase : Optional[Any] = re.sub(R'/pre_mlp_layer_norm/' , R'/1/layer_norm/' , UpperCAmelCase_ )
elif groups[0] == "decoder":
UpperCAmelCase : Any = re.sub(R'/mlp/' , R'/2/mlp/' , UpperCAmelCase_ )
UpperCAmelCase : Optional[Any] = re.sub(R'/pre_mlp_layer_norm/' , R'/2/layer_norm/' , UpperCAmelCase_ )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
UpperCAmelCase : Optional[int] = new_key.replace(UpperCAmelCase_ , UpperCAmelCase_ )
print(F"""{key} -> {new_key}""" )
UpperCAmelCase : Optional[int] = s_dict.pop(UpperCAmelCase_ )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase : Union[str, Any] = s_dict[
'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase : List[str] = s_dict[
'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
UpperCAmelCase : str = s_dict[key].shape[0]
UpperCAmelCase : List[str] = s_dict[key]
for idx in range(UpperCAmelCase_ ):
UpperCAmelCase : Optional[Any] = expert_weihts[idx]
print(F"""{key} -> {key.replace("expert/" , "nested fstring" )}""" )
s_dict.pop(UpperCAmelCase_ )
return s_dict
lowercase__ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ) -> Any:
# Convert a google style config to the hugging face fromat
import regex as re
with open(UpperCAmelCase_ , 'r' ) as f:
UpperCAmelCase : Any = f.read()
UpperCAmelCase : Union[str, Any] = re.findall(R'(.*) = ([0-9.]*)' , UpperCAmelCase_ )
UpperCAmelCase : Dict = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
UpperCAmelCase : Optional[Any] = float(UpperCAmelCase_ ) if '.' in value else int(UpperCAmelCase_ )
UpperCAmelCase : List[Any] = re.findall(R'(.*activations) = \(\'(.*)\',\)' , UpperCAmelCase_ )[0]
UpperCAmelCase : Tuple = str(activation[1] )
UpperCAmelCase : Dict = num_experts
UpperCAmelCase : Any = SwitchTransformersConfig(**UpperCAmelCase_ )
return config
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=None , UpperCAmelCase_="./" , UpperCAmelCase_=8 ) -> str:
# Initialise PyTorch model
print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
UpperCAmelCase : Union[str, Any] = checkpoints.load_tax_checkpoint(UpperCAmelCase_ )
if gin_file is not None:
UpperCAmelCase : Optional[int] = convert_gin_to_config(UpperCAmelCase_ , UpperCAmelCase_ )
else:
UpperCAmelCase : int = SwitchTransformersConfig.from_pretrained(UpperCAmelCase_ )
UpperCAmelCase : Dict = SwitchTransformersForConditionalGeneration(UpperCAmelCase_ )
UpperCAmelCase : Union[str, Any] = flax_params['target']
UpperCAmelCase : Tuple = flatten_dict(UpperCAmelCase_ , sep='/' )
UpperCAmelCase : List[str] = rename_keys(UpperCAmelCase_ )
UpperCAmelCase : Dict = unflatten_dict(UpperCAmelCase_ , sep='/' )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(UpperCAmelCase_ , UpperCAmelCase_ )
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
pt_model.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase__ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 714 |
'''simple docstring'''
import baseaa
def UpperCamelCase( UpperCAmelCase_ ):
return baseaa.baaencode(string.encode('utf-8' ) )
def UpperCamelCase( UpperCAmelCase_ ):
return baseaa.baadecode(UpperCAmelCase_ ).decode('utf-8' )
if __name__ == "__main__":
lowercase__ = "Hello World!"
lowercase__ = baseaa_encode(test)
print(encoded)
lowercase__ = baseaa_decode(encoded)
print(decoded)
| 695 | 0 |
def UpperCamelCase( ):
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
lowercase__ = generate_large_matrix()
lowercase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def UpperCamelCase( UpperCAmelCase_ ):
assert all(row == sorted(UpperCAmelCase_ , reverse=UpperCAmelCase_ ) for row in grid )
assert all(list(UpperCAmelCase_ ) == sorted(UpperCAmelCase_ , reverse=UpperCAmelCase_ ) for col in zip(*UpperCAmelCase_ ) )
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : int = 0
UpperCAmelCase : List[Any] = len(UpperCAmelCase_ ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
UpperCAmelCase : List[str] = (left + right) // 2
UpperCAmelCase : Optional[int] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
UpperCAmelCase : Optional[int] = mid + 1
else:
UpperCAmelCase : List[Any] = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(UpperCAmelCase_ )
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : List[str] = 0
UpperCAmelCase : int = len(grid[0] )
for i in range(len(UpperCAmelCase_ ) ):
UpperCAmelCase : List[str] = find_negative_index(grid[i][:bound] )
total += bound
return (len(UpperCAmelCase_ ) * len(grid[0] )) - total
def UpperCamelCase( UpperCAmelCase_ ):
return len([number for row in grid for number in row if number < 0] )
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : str = 0
for row in grid:
for i, number in enumerate(UpperCAmelCase_ ):
if number < 0:
total += len(UpperCAmelCase_ ) - i
break
return total
def UpperCamelCase( ):
from timeit import timeit
print('Running benchmarks' )
UpperCAmelCase : int = (
'from __main__ import count_negatives_binary_search, '
'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
UpperCAmelCase : Tuple = timeit(F"""{func}(grid=grid)""" , setup=UpperCAmelCase_ , number=5_00 )
print(F"""{func}() took {time:0.4f} seconds""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 715 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(UpperCAmelCase_ , UpperCAmelCase_ ) ) )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if dataset.ndim != value_array.ndim:
UpperCAmelCase : str = (
'Wrong input data\'s dimensions... '
F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(UpperCAmelCase_ )
try:
if dataset.shape[1] != value_array.shape[1]:
UpperCAmelCase : str = (
'Wrong input data\'s shape... '
F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(UpperCAmelCase_ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('Wrong shape' )
if dataset.dtype != value_array.dtype:
UpperCAmelCase : List[str] = (
'Input data have different datatype... '
F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(UpperCAmelCase_ )
UpperCAmelCase : str = []
for value in value_array:
UpperCAmelCase : Optional[Any] = euclidean(UpperCAmelCase_ , dataset[0] )
UpperCAmelCase : Tuple = dataset[0].tolist()
for dataset_value in dataset[1:]:
UpperCAmelCase : Tuple = euclidean(UpperCAmelCase_ , UpperCAmelCase_ )
if dist > temp_dist:
UpperCAmelCase : List[str] = temp_dist
UpperCAmelCase : str = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return np.dot(UpperCAmelCase_ , UpperCAmelCase_ ) / (norm(UpperCAmelCase_ ) * norm(UpperCAmelCase_ ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 695 | 0 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
# Return True if there is node that has not iterated.
UpperCAmelCase : Dict = [False] * len(UpperCAmelCase_ )
UpperCAmelCase : Optional[Any] = []
queue.append(UpperCAmelCase_ )
UpperCAmelCase : int = True
while queue:
UpperCAmelCase : Any = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(UpperCAmelCase_ )
UpperCAmelCase : Dict = True
UpperCAmelCase : List[Any] = u
return visited[t]
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
# This array is filled by BFS and to store path
UpperCAmelCase : str = [-1] * (len(UpperCAmelCase_ ))
UpperCAmelCase : Optional[int] = 0
while bfs(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = float('Inf' )
UpperCAmelCase : Optional[int] = sink
while s != source:
# Find the minimum value in select path
UpperCAmelCase : Optional[int] = min(UpperCAmelCase_ , graph[parent[s]][s] )
UpperCAmelCase : Dict = parent[s]
max_flow += path_flow
UpperCAmelCase : Optional[int] = sink
while v != source:
UpperCAmelCase : Any = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
UpperCAmelCase : Dict = parent[v]
return max_flow
lowercase__ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
lowercase__ , lowercase__ = 0, 5
print(ford_fulkerson(graph, source, sink))
| 716 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
lowercase__ = get_tests_dir("fixtures")
lowercase__ = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
lowercase__ = get_tests_dir("fixtures/dummy-config.json")
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
UpperCAmelCase : Optional[Any] = 0
def UpperCAmelCase_ ( self : List[Any] ) -> Any:
UpperCAmelCase : Optional[int] = AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
UpperCAmelCase : str = AutoFeatureExtractor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase : Any = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
UpperCAmelCase : Union[str, Any] = AutoFeatureExtractor.from_pretrained(lowercase_ ).to_dict()
config_dict.pop('feature_extractor_type' )
UpperCAmelCase : List[Any] = WavaVecaFeatureExtractor(**lowercase_ )
# save in new folder
model_config.save_pretrained(lowercase_ )
config.save_pretrained(lowercase_ )
UpperCAmelCase : Dict = AutoFeatureExtractor.from_pretrained(lowercase_ )
# make sure private variable is not incorrectly saved
UpperCAmelCase : List[Any] = json.loads(config.to_json_string() )
self.assertTrue('_processor_class' not in dict_as_saved )
self.assertIsInstance(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
UpperCAmelCase : List[str] = AutoFeatureExtractor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
with self.assertRaisesRegex(
lowercase_ , 'bert-base is not a local folder and is not a valid model identifier' ):
UpperCAmelCase : Optional[int] = AutoFeatureExtractor.from_pretrained('bert-base' )
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
with self.assertRaisesRegex(
lowercase_ , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
UpperCAmelCase : int = AutoFeatureExtractor.from_pretrained(lowercase_ , revision='aaaaaa' )
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
with self.assertRaisesRegex(
lowercase_ , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
UpperCAmelCase : List[str] = AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model' )
def UpperCAmelCase_ ( self : Optional[int] ) -> int:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowercase_ ):
UpperCAmelCase : Any = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowercase_ ):
UpperCAmelCase : Union[str, Any] = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase_ )
UpperCAmelCase : Optional[int] = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase_ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowercase_ )
UpperCAmelCase : str = AutoFeatureExtractor.from_pretrained(lowercase_ , trust_remote_code=lowercase_ )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
try:
AutoConfig.register('custom' , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase_ ):
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
UpperCAmelCase : Dict = CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(lowercase_ )
UpperCAmelCase : Union[str, Any] = AutoFeatureExtractor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase_ ( self : int ) -> Tuple:
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = True
try:
AutoConfig.register('custom' , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
# If remote code is not set, the default is to use local
UpperCAmelCase : Optional[int] = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
UpperCAmelCase : Dict = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase_ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
UpperCAmelCase : Tuple = AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase_ )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
self.assertTrue(not hasattr(lowercase_ , 'is_local' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 695 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Dict = """megatron-bert"""
def __init__( self : List[Any] , lowercase_ : Tuple=29_056 , lowercase_ : Any=1_024 , lowercase_ : int=24 , lowercase_ : Optional[int]=16 , lowercase_ : List[str]=4_096 , lowercase_ : str="gelu" , lowercase_ : List[str]=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : Tuple=512 , lowercase_ : Optional[Any]=2 , lowercase_ : Any=0.02 , lowercase_ : str=1E-12 , lowercase_ : Any=0 , lowercase_ : Tuple="absolute" , lowercase_ : Tuple=True , **lowercase_ : Any , ) -> Any:
super().__init__(pad_token_id=lowercase_ , **lowercase_ )
UpperCAmelCase : Dict = vocab_size
UpperCAmelCase : Dict = hidden_size
UpperCAmelCase : Tuple = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : Optional[int] = hidden_act
UpperCAmelCase : List[str] = intermediate_size
UpperCAmelCase : int = hidden_dropout_prob
UpperCAmelCase : Any = attention_probs_dropout_prob
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : List[Any] = type_vocab_size
UpperCAmelCase : Tuple = initializer_range
UpperCAmelCase : List[Any] = layer_norm_eps
UpperCAmelCase : Dict = position_embedding_type
UpperCAmelCase : List[str] = use_cache
| 717 |
'''simple docstring'''
from datetime import datetime
import requests
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Tuple = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
UpperCAmelCase : List[str] = requests.get(base_url + url ).json()[0]['urls'][0]['src']
return requests.get(UpperCAmelCase_ ).content
if __name__ == "__main__":
lowercase__ = input("Enter Video/IGTV url: ").strip()
lowercase__ = f'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(f'''Done. Video saved to disk as {file_name}.''')
| 695 | 0 |
import math
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return math.pow(UpperCAmelCase_ , 2 ) - a
def UpperCamelCase( UpperCAmelCase_ ):
return 2 * x
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : str = 2.0
while start <= a:
UpperCAmelCase : str = math.pow(UpperCAmelCase_ , 2 )
return start
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ = 99_99 , UpperCAmelCase_ = 0.00_0000_0000_0001 ):
if a < 0:
raise ValueError('math domain error' )
UpperCAmelCase : int = get_initial_point(UpperCAmelCase_ )
for _ in range(UpperCAmelCase_ ):
UpperCAmelCase : Any = value
UpperCAmelCase : List[Any] = value - fx(UpperCAmelCase_ , UpperCAmelCase_ ) / fx_derivative(UpperCAmelCase_ )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 718 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ = 10**9 ):
UpperCAmelCase : Union[str, Any] = 1
UpperCAmelCase : Optional[int] = 2
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Union[str, Any] = 0
UpperCAmelCase : List[Any] = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
UpperCAmelCase : Dict = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f'''{solution() = }''')
| 695 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A_ ( _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = UnCLIPImageVariationPipeline
UpperCAmelCase_ : Any = IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
UpperCAmelCase_ : List[Any] = IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase_ : str = [
"""generator""",
"""return_dict""",
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
UpperCAmelCase_ : Dict = False
@property
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
return 32
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]:
return 32
@property
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
return self.time_input_dim
@property
def UpperCAmelCase_ ( self : Dict ) -> Tuple:
return self.time_input_dim * 4
@property
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
return 100
@property
def UpperCAmelCase_ ( self : str ) -> List[Any]:
UpperCAmelCase : List[str] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
torch.manual_seed(0 )
UpperCAmelCase : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModelWithProjection(lowercase_ )
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
torch.manual_seed(0 )
UpperCAmelCase : Union[str, Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(lowercase_ )
@property
def UpperCAmelCase_ ( self : Dict ) -> Optional[int]:
torch.manual_seed(0 )
UpperCAmelCase : Union[str, Any] = {
'clip_embeddings_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'cross_attention_dim': self.cross_attention_dim,
}
UpperCAmelCase : List[Any] = UnCLIPTextProjModel(**lowercase_ )
return model
@property
def UpperCAmelCase_ ( self : Any ) -> Union[str, Any]:
torch.manual_seed(0 )
UpperCAmelCase : List[str] = {
'sample_size': 32,
# RGB in channels
'in_channels': 3,
# Out channels is double in channels because predicts mean and variance
'out_channels': 6,
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': 'identity',
}
UpperCAmelCase : List[Any] = UNetaDConditionModel(**lowercase_ )
return model
@property
def UpperCAmelCase_ ( self : Any ) -> Any:
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def UpperCAmelCase_ ( self : str ) -> Tuple:
torch.manual_seed(0 )
UpperCAmelCase : Optional[Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> List[str]:
# seeded differently to get different unet than `self.dummy_super_res_first`
torch.manual_seed(1 )
UpperCAmelCase : List[str] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def UpperCAmelCase_ ( self : Any ) -> Any:
UpperCAmelCase : Tuple = self.dummy_decoder
UpperCAmelCase : List[Any] = self.dummy_text_proj
UpperCAmelCase : str = self.dummy_text_encoder
UpperCAmelCase : List[Any] = self.dummy_tokenizer
UpperCAmelCase : Any = self.dummy_super_res_first
UpperCAmelCase : Optional[Any] = self.dummy_super_res_last
UpperCAmelCase : Any = UnCLIPScheduler(
variance_type='learned_range' , prediction_type='epsilon' , num_train_timesteps=1_000 , )
UpperCAmelCase : Any = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='epsilon' , num_train_timesteps=1_000 , )
UpperCAmelCase : Union[str, Any] = CLIPImageProcessor(crop_size=32 , size=32 )
UpperCAmelCase : Union[str, Any] = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def UpperCAmelCase_ ( self : Tuple , lowercase_ : List[str] , lowercase_ : Any=0 , lowercase_ : Union[str, Any]=True ) -> List[str]:
UpperCAmelCase : str = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
if str(lowercase_ ).startswith('mps' ):
UpperCAmelCase : Dict = torch.manual_seed(lowercase_ )
else:
UpperCAmelCase : List[Any] = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
if pil_image:
UpperCAmelCase : Tuple = input_image * 0.5 + 0.5
UpperCAmelCase : Union[str, Any] = input_image.clamp(0 , 1 )
UpperCAmelCase : Optional[Any] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCAmelCase : str = DiffusionPipeline.numpy_to_pil(lowercase_ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def UpperCAmelCase_ ( self : str ) -> Any:
UpperCAmelCase : Any = 'cpu'
UpperCAmelCase : int = self.get_dummy_components()
UpperCAmelCase : Optional[int] = self.pipeline_class(**lowercase_ )
UpperCAmelCase : Optional[int] = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : int = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : int = pipe(**lowercase_ )
UpperCAmelCase : str = output.images
UpperCAmelCase : Dict = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : List[Any] = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
UpperCAmelCase : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : Optional[Any] = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase_ ( self : Dict ) -> str:
UpperCAmelCase : Dict = 'cpu'
UpperCAmelCase : Any = self.get_dummy_components()
UpperCAmelCase : List[Any] = self.pipeline_class(**lowercase_ )
UpperCAmelCase : Tuple = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Tuple = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : Union[str, Any] = pipe(**lowercase_ )
UpperCAmelCase : List[Any] = output.images
UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : List[str] = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
UpperCAmelCase : Tuple = image[0, -3:, -3:, -1]
UpperCAmelCase : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : Dict = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase_ ( self : List[Any] ) -> int:
UpperCAmelCase : List[Any] = 'cpu'
UpperCAmelCase : int = self.get_dummy_components()
UpperCAmelCase : int = self.pipeline_class(**lowercase_ )
UpperCAmelCase : Tuple = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Any = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : List[Any] = [
pipeline_inputs['image'],
pipeline_inputs['image'],
]
UpperCAmelCase : List[Any] = pipe(**lowercase_ )
UpperCAmelCase : Tuple = output.images
UpperCAmelCase : str = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : Optional[int] = [
tuple_pipeline_inputs['image'],
tuple_pipeline_inputs['image'],
]
UpperCAmelCase : Dict = pipe(
**lowercase_ , return_dict=lowercase_ , )[0]
UpperCAmelCase : Tuple = image[0, -3:, -3:, -1]
UpperCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
UpperCAmelCase : int = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase_ ( self : Any ) -> Tuple:
UpperCAmelCase : Dict = torch.device('cpu' )
class A_ :
'''simple docstring'''
UpperCAmelCase_ : List[Any] = 1
UpperCAmelCase : int = self.get_dummy_components()
UpperCAmelCase : List[Any] = self.pipeline_class(**lowercase_ )
UpperCAmelCase : int = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Tuple = torch.Generator(device=lowercase_ ).manual_seed(0 )
UpperCAmelCase : Tuple = pipe.decoder.dtype
UpperCAmelCase : Union[str, Any] = 1
UpperCAmelCase : Optional[Any] = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
UpperCAmelCase : Any = pipe.prepare_latents(
lowercase_ , dtype=lowercase_ , device=lowercase_ , generator=lowercase_ , latents=lowercase_ , scheduler=DummyScheduler() )
UpperCAmelCase : List[str] = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
UpperCAmelCase : List[Any] = pipe.prepare_latents(
lowercase_ , dtype=lowercase_ , device=lowercase_ , generator=lowercase_ , latents=lowercase_ , scheduler=DummyScheduler() )
UpperCAmelCase : List[str] = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
UpperCAmelCase : Any = pipe(
**lowercase_ , decoder_latents=lowercase_ , super_res_latents=lowercase_ ).images
UpperCAmelCase : int = self.get_dummy_inputs(lowercase_ , pil_image=lowercase_ )
# Don't pass image, instead pass embedding
UpperCAmelCase : Any = pipeline_inputs.pop('image' )
UpperCAmelCase : List[str] = pipe.image_encoder(lowercase_ ).image_embeds
UpperCAmelCase : str = pipe(
**lowercase_ , decoder_latents=lowercase_ , super_res_latents=lowercase_ , image_embeddings=lowercase_ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
UpperCAmelCase : Any = torch_device == 'cpu'
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
UpperCAmelCase : Tuple = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=lowercase_ , expected_max_diff=lowercase_ )
@skip_mps
def UpperCAmelCase_ ( self : str ) -> List[str]:
UpperCAmelCase : Dict = torch_device == 'cpu'
UpperCAmelCase : Optional[int] = True
UpperCAmelCase : Tuple = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
self._test_inference_batch_single_identical(
test_max_difference=lowercase_ , relax_max_difference=lowercase_ , additional_params_copy_to_batched_inputs=lowercase_ , )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = [
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
UpperCAmelCase : List[str] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=lowercase_ , additional_params_copy_to_batched_inputs=lowercase_ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=lowercase_ )
@skip_mps
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def UpperCAmelCase_ ( self : Any ) -> Dict:
return super().test_save_load_local()
@skip_mps
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Any ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
UpperCAmelCase : Optional[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )
UpperCAmelCase : List[Any] = UnCLIPImageVariationPipeline.from_pretrained(
'kakaobrain/karlo-v1-alpha-image-variations' , torch_dtype=torch.floataa )
UpperCAmelCase : Dict = pipeline.to(lowercase_ )
pipeline.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : str = torch.Generator(device='cpu' ).manual_seed(0 )
UpperCAmelCase : Dict = pipeline(
lowercase_ , generator=lowercase_ , output_type='np' , )
UpperCAmelCase : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_ , 15 )
| 719 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( unittest.TestCase ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self : Any ) -> List[Any]:
torch.manual_seed(0 )
UpperCAmelCase : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> int:
torch.manual_seed(0 )
UpperCAmelCase : str = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , )
return model
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> List[str]:
torch.manual_seed(0 )
UpperCAmelCase : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(lowercase_ )
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
UpperCAmelCase : Any = self.dummy_uncond_unet
UpperCAmelCase : Tuple = DDIMScheduler()
UpperCAmelCase : Optional[Any] = self.dummy_vq_model
UpperCAmelCase : str = LDMPipeline(unet=lowercase_ , vqvae=lowercase_ , scheduler=lowercase_ )
ldm.to(lowercase_ )
ldm.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : str = torch.manual_seed(0 )
UpperCAmelCase : int = ldm(generator=lowercase_ , num_inference_steps=2 , output_type='numpy' ).images
UpperCAmelCase : int = torch.manual_seed(0 )
UpperCAmelCase : Tuple = ldm(generator=lowercase_ , num_inference_steps=2 , output_type='numpy' , return_dict=lowercase_ )[0]
UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : List[str] = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
UpperCAmelCase : Tuple = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Tuple ) -> Any:
UpperCAmelCase : Any = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(lowercase_ )
ldm.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Tuple = torch.manual_seed(0 )
UpperCAmelCase : Dict = ldm(generator=lowercase_ , num_inference_steps=5 , output_type='numpy' ).images
UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Optional[int] = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
UpperCAmelCase : Any = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 695 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : jnp.ndarray
UpperCAmelCase_ : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase_ : int
UpperCAmelCase_ : Tuple[int] = (16, 32, 96, 256)
UpperCAmelCase_ : jnp.dtype = jnp.floataa
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
UpperCAmelCase : Any = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
UpperCAmelCase : Optional[Any] = []
for i in range(len(self.block_out_channels ) - 1 ):
UpperCAmelCase : Optional[int] = self.block_out_channels[i]
UpperCAmelCase : Dict = self.block_out_channels[i + 1]
UpperCAmelCase : Tuple = nn.Conv(
lowercase_ , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(lowercase_ )
UpperCAmelCase : Optional[int] = nn.Conv(
lowercase_ , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(lowercase_ )
UpperCAmelCase : Tuple = blocks
UpperCAmelCase : Optional[Any] = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self : str , lowercase_ : List[Any] ) -> Dict:
UpperCAmelCase : Optional[Any] = self.conv_in(lowercase_ )
UpperCAmelCase : str = nn.silu(lowercase_ )
for block in self.blocks:
UpperCAmelCase : int = block(lowercase_ )
UpperCAmelCase : Tuple = nn.silu(lowercase_ )
UpperCAmelCase : Tuple = self.conv_out(lowercase_ )
return embedding
@flax_register_to_config
class A_ ( nn.Module , _snake_case , _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : int = 32
UpperCAmelCase_ : int = 4
UpperCAmelCase_ : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
UpperCAmelCase_ : Union[bool, Tuple[bool]] = False
UpperCAmelCase_ : Tuple[int] = (320, 640, 1_280, 1_280)
UpperCAmelCase_ : int = 2
UpperCAmelCase_ : Union[int, Tuple[int]] = 8
UpperCAmelCase_ : Optional[Union[int, Tuple[int]]] = None
UpperCAmelCase_ : int = 1_280
UpperCAmelCase_ : float = 0.0
UpperCAmelCase_ : bool = False
UpperCAmelCase_ : jnp.dtype = jnp.floataa
UpperCAmelCase_ : bool = True
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : str = "rgb"
UpperCAmelCase_ : Tuple[int] = (16, 32, 96, 256)
def UpperCAmelCase_ ( self : Union[str, Any] , lowercase_ : jax.random.KeyArray ) -> FrozenDict:
# init input tensors
UpperCAmelCase : Tuple = (1, self.in_channels, self.sample_size, self.sample_size)
UpperCAmelCase : Tuple = jnp.zeros(lowercase_ , dtype=jnp.floataa )
UpperCAmelCase : Tuple = jnp.ones((1,) , dtype=jnp.intaa )
UpperCAmelCase : Union[str, Any] = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
UpperCAmelCase : Optional[int] = (1, 3, self.sample_size * 8, self.sample_size * 8)
UpperCAmelCase : str = jnp.zeros(lowercase_ , dtype=jnp.floataa )
UpperCAmelCase : Tuple = jax.random.split(lowercase_ )
UpperCAmelCase : Dict = {'params': params_rng, 'dropout': dropout_rng}
return self.init(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )["params"]
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
UpperCAmelCase : Any = self.block_out_channels
UpperCAmelCase : Tuple = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
UpperCAmelCase : List[Any] = self.num_attention_heads or self.attention_head_dim
# input
UpperCAmelCase : Optional[Any] = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
UpperCAmelCase : List[Any] = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
UpperCAmelCase : Optional[int] = FlaxTimestepEmbedding(lowercase_ , dtype=self.dtype )
UpperCAmelCase : int = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
UpperCAmelCase : List[Any] = self.only_cross_attention
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase : Optional[Any] = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase : List[Any] = (num_attention_heads,) * len(self.down_block_types )
# down
UpperCAmelCase : Union[str, Any] = []
UpperCAmelCase : Optional[Any] = []
UpperCAmelCase : Optional[int] = block_out_channels[0]
UpperCAmelCase : str = nn.Conv(
lowercase_ , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowercase_ )
for i, down_block_type in enumerate(self.down_block_types ):
UpperCAmelCase : Union[str, Any] = output_channel
UpperCAmelCase : Union[str, Any] = block_out_channels[i]
UpperCAmelCase : Dict = i == len(lowercase_ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
UpperCAmelCase : str = FlaxCrossAttnDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
UpperCAmelCase : Tuple = FlaxDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(lowercase_ )
for _ in range(self.layers_per_block ):
UpperCAmelCase : Any = nn.Conv(
lowercase_ , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowercase_ )
if not is_final_block:
UpperCAmelCase : Any = nn.Conv(
lowercase_ , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowercase_ )
UpperCAmelCase : int = down_blocks
UpperCAmelCase : Dict = controlnet_down_blocks
# mid
UpperCAmelCase : Union[str, Any] = block_out_channels[-1]
UpperCAmelCase : Any = FlaxUNetMidBlockaDCrossAttn(
in_channels=lowercase_ , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
UpperCAmelCase : Optional[Any] = nn.Conv(
lowercase_ , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self : Union[str, Any] , lowercase_ : Any , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Any , lowercase_ : float = 1.0 , lowercase_ : bool = True , lowercase_ : bool = False , ) -> Union[FlaxControlNetOutput, Tuple]:
UpperCAmelCase : str = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
UpperCAmelCase : Any = jnp.flip(lowercase_ , axis=1 )
# 1. time
if not isinstance(lowercase_ , jnp.ndarray ):
UpperCAmelCase : Any = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowercase_ , jnp.ndarray ) and len(timesteps.shape ) == 0:
UpperCAmelCase : Optional[Any] = timesteps.astype(dtype=jnp.floataa )
UpperCAmelCase : Any = jnp.expand_dims(lowercase_ , 0 )
UpperCAmelCase : Any = self.time_proj(lowercase_ )
UpperCAmelCase : List[Any] = self.time_embedding(lowercase_ )
# 2. pre-process
UpperCAmelCase : Dict = jnp.transpose(lowercase_ , (0, 2, 3, 1) )
UpperCAmelCase : Optional[int] = self.conv_in(lowercase_ )
UpperCAmelCase : Optional[int] = jnp.transpose(lowercase_ , (0, 2, 3, 1) )
UpperCAmelCase : Any = self.controlnet_cond_embedding(lowercase_ )
sample += controlnet_cond
# 3. down
UpperCAmelCase : List[str] = (sample,)
for down_block in self.down_blocks:
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase : int = down_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
else:
UpperCAmelCase : Union[str, Any] = down_block(lowercase_ , lowercase_ , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
UpperCAmelCase : Tuple = self.mid_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
# 5. contronet blocks
UpperCAmelCase : Optional[int] = ()
for down_block_res_sample, controlnet_block in zip(lowercase_ , self.controlnet_down_blocks ):
UpperCAmelCase : Tuple = controlnet_block(lowercase_ )
controlnet_down_block_res_samples += (down_block_res_sample,)
UpperCAmelCase : Union[str, Any] = controlnet_down_block_res_samples
UpperCAmelCase : List[str] = self.controlnet_mid_block(lowercase_ )
# 6. scaling
UpperCAmelCase : Dict = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=lowercase_ , mid_block_res_sample=lowercase_ )
| 720 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( unittest.TestCase ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
torch.manual_seed(0 )
UpperCAmelCase : Any = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
UpperCAmelCase : Dict = self.dummy_uncond_unet
UpperCAmelCase : Dict = KarrasVeScheduler()
UpperCAmelCase : str = KarrasVePipeline(unet=lowercase_ , scheduler=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
UpperCAmelCase : Optional[int] = pipe(num_inference_steps=2 , generator=lowercase_ , output_type='numpy' ).images
UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
UpperCAmelCase : Optional[Any] = pipe(num_inference_steps=2 , generator=lowercase_ , output_type='numpy' , return_dict=lowercase_ )[0]
UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase : Any = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple:
UpperCAmelCase : Dict = 'google/ncsnpp-celebahq-256'
UpperCAmelCase : Any = UNetaDModel.from_pretrained(lowercase_ )
UpperCAmelCase : Union[str, Any] = KarrasVeScheduler()
UpperCAmelCase : Dict = KarrasVePipeline(unet=lowercase_ , scheduler=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCAmelCase : Union[str, Any] = torch.manual_seed(0 )
UpperCAmelCase : Dict = pipe(num_inference_steps=20 , generator=lowercase_ , output_type='numpy' ).images
UpperCAmelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase : Optional[int] = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 695 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}
# fmt: off
lowercase__ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
lowercase__ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = """whisper"""
UpperCAmelCase_ : Tuple = ["""past_key_values"""]
UpperCAmelCase_ : Union[str, Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : str , lowercase_ : Any=51_865 , lowercase_ : List[Any]=80 , lowercase_ : int=6 , lowercase_ : Dict=4 , lowercase_ : List[Any]=6 , lowercase_ : Any=4 , lowercase_ : Tuple=1_536 , lowercase_ : Tuple=1_536 , lowercase_ : Tuple=0.0 , lowercase_ : Optional[int]=0.0 , lowercase_ : List[Any]=50_257 , lowercase_ : Optional[int]=True , lowercase_ : Any=True , lowercase_ : str="gelu" , lowercase_ : List[str]=256 , lowercase_ : str=0.0 , lowercase_ : Any=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Dict=0.02 , lowercase_ : Optional[int]=False , lowercase_ : Union[str, Any]=1_500 , lowercase_ : List[Any]=448 , lowercase_ : int=50_256 , lowercase_ : Union[str, Any]=50_256 , lowercase_ : List[Any]=50_256 , lowercase_ : Tuple=None , lowercase_ : Optional[Any]=[220, 50_256] , lowercase_ : Tuple=False , lowercase_ : str=256 , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=0.05 , lowercase_ : Any=10 , lowercase_ : Optional[Any]=2 , lowercase_ : Optional[Any]=0.0 , lowercase_ : Optional[int]=10 , lowercase_ : int=0 , lowercase_ : Optional[int]=7 , **lowercase_ : Union[str, Any] , ) -> List[str]:
UpperCAmelCase : Optional[Any] = vocab_size
UpperCAmelCase : Any = num_mel_bins
UpperCAmelCase : List[Any] = d_model
UpperCAmelCase : int = encoder_layers
UpperCAmelCase : str = encoder_attention_heads
UpperCAmelCase : Tuple = decoder_layers
UpperCAmelCase : Any = decoder_attention_heads
UpperCAmelCase : Tuple = decoder_ffn_dim
UpperCAmelCase : List[str] = encoder_ffn_dim
UpperCAmelCase : int = dropout
UpperCAmelCase : int = attention_dropout
UpperCAmelCase : List[Any] = activation_dropout
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : Union[str, Any] = init_std
UpperCAmelCase : Dict = encoder_layerdrop
UpperCAmelCase : str = decoder_layerdrop
UpperCAmelCase : Union[str, Any] = use_cache
UpperCAmelCase : int = encoder_layers
UpperCAmelCase : int = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Tuple = max_source_positions
UpperCAmelCase : List[Any] = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : Optional[int] = classifier_proj_size
UpperCAmelCase : List[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : Optional[Any] = mask_time_prob
UpperCAmelCase : Optional[Any] = mask_time_length
UpperCAmelCase : str = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Tuple = mask_feature_length
UpperCAmelCase : Optional[int] = mask_feature_min_masks
UpperCAmelCase : str = median_filter_width
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , suppress_tokens=lowercase_ , begin_suppress_tokens=lowercase_ , **lowercase_ , )
class A_ ( _snake_case ):
'''simple docstring'''
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : Optional[int] = OrderedDict(
[
('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
] )
if self.use_past:
UpperCAmelCase : int = {0: 'batch'}
else:
UpperCAmelCase : List[str] = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='inputs' )
return common_inputs
def UpperCAmelCase_ ( self : Optional[Any] , lowercase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional["TensorType"] = None , lowercase_ : int = 22_050 , lowercase_ : float = 5.0 , lowercase_ : int = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Tuple = OrderedDict()
UpperCAmelCase : Tuple = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=lowercase_ , framework=lowercase_ , sampling_rate=lowercase_ , time_duration=lowercase_ , frequency=lowercase_ , )
UpperCAmelCase : Optional[Any] = encoder_inputs['input_features'].shape[2]
UpperCAmelCase : Tuple = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Optional[int] = super().generate_dummy_inputs(
preprocessor.tokenizer , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase : Dict = encoder_inputs.pop('input_features' )
UpperCAmelCase : List[str] = decoder_inputs.pop('decoder_input_ids' )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop('past_key_values' )
return dummy_inputs
@property
def UpperCAmelCase_ ( self : Dict ) -> float:
return 1E-3
| 721 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = """autoformer"""
UpperCAmelCase_ : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__( self : Dict , lowercase_ : Optional[int] = None , lowercase_ : Optional[int] = None , lowercase_ : str = "student_t" , lowercase_ : str = "nll" , lowercase_ : int = 1 , lowercase_ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowercase_ : bool = True , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : Optional[List[int]] = None , lowercase_ : Optional[List[int]] = None , lowercase_ : int = 64 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : int = 32 , lowercase_ : int = 32 , lowercase_ : str = "gelu" , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : int = 100 , lowercase_ : float = 0.02 , lowercase_ : bool = True , lowercase_ : Union[str, Any]=True , lowercase_ : int = 10 , lowercase_ : int = 25 , lowercase_ : int = 3 , **lowercase_ : str , ) -> Dict:
# time series specific configuration
UpperCAmelCase : int = prediction_length
UpperCAmelCase : Optional[Any] = context_length if context_length is not None else prediction_length
UpperCAmelCase : List[Any] = distribution_output
UpperCAmelCase : Tuple = loss
UpperCAmelCase : Dict = input_size
UpperCAmelCase : Dict = num_time_features
UpperCAmelCase : Tuple = lags_sequence
UpperCAmelCase : str = scaling
UpperCAmelCase : Optional[int] = num_dynamic_real_features
UpperCAmelCase : List[str] = num_static_real_features
UpperCAmelCase : Optional[int] = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(lowercase_ ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
UpperCAmelCase : int = cardinality
else:
UpperCAmelCase : Union[str, Any] = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(lowercase_ ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
UpperCAmelCase : Any = embedding_dimension
else:
UpperCAmelCase : int = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
UpperCAmelCase : Dict = num_parallel_samples
# Transformer architecture configuration
UpperCAmelCase : Optional[int] = input_size * len(self.lags_sequence ) + self._number_of_features
UpperCAmelCase : List[Any] = d_model
UpperCAmelCase : Dict = encoder_attention_heads
UpperCAmelCase : Tuple = decoder_attention_heads
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : str = decoder_ffn_dim
UpperCAmelCase : str = encoder_layers
UpperCAmelCase : Optional[Any] = decoder_layers
UpperCAmelCase : int = dropout
UpperCAmelCase : Any = attention_dropout
UpperCAmelCase : Tuple = activation_dropout
UpperCAmelCase : str = encoder_layerdrop
UpperCAmelCase : Union[str, Any] = decoder_layerdrop
UpperCAmelCase : Tuple = activation_function
UpperCAmelCase : Dict = init_std
UpperCAmelCase : Union[str, Any] = use_cache
# Autoformer
UpperCAmelCase : Any = label_length
UpperCAmelCase : List[Any] = moving_average
UpperCAmelCase : Optional[Any] = autocorrelation_factor
super().__init__(is_encoder_decoder=lowercase_ , **lowercase_ )
@property
def UpperCAmelCase_ ( self : List[str] ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 695 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Any = ['image_processor', 'tokenizer']
lowerCamelCase__ : Optional[int] = 'ViTImageProcessor'
lowerCamelCase__ : Dict = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__(self, lowerCamelCase_=None, lowerCamelCase_=None, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : int = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.', lowerCamelCase_, )
lowerCamelCase__ : Optional[Any] = kwargs.pop('feature_extractor' )
lowerCamelCase__ : str = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(lowerCamelCase_, lowerCamelCase_ )
def __call__(self, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, **lowerCamelCase_ ):
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError('You have to specify either text, visual prompt or images.' )
if text is not None and visual_prompt is not None:
raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.' )
if text is not None:
lowerCamelCase__ : Dict = self.tokenizer(lowerCamelCase_, return_tensors=lowerCamelCase_, **lowerCamelCase_ )
if visual_prompt is not None:
lowerCamelCase__ : Any = self.image_processor(lowerCamelCase_, return_tensors=lowerCamelCase_, **lowerCamelCase_ )
if images is not None:
lowerCamelCase__ : Tuple = self.image_processor(lowerCamelCase_, return_tensors=lowerCamelCase_, **lowerCamelCase_ )
if visual_prompt is not None and images is not None:
lowerCamelCase__ : Any = {
'pixel_values': image_features.pixel_values,
'conditional_pixel_values': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
lowerCamelCase__ : str = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
lowerCamelCase__ : Optional[int] = {
'conditional_pixel_values': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowerCamelCase_ ), tensor_type=lowerCamelCase_ )
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase_, **lowerCamelCase_ )
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase_, **lowerCamelCase_ )
@property
def a__ (self ):
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.', lowerCamelCase_, )
return self.image_processor_class
@property
def a__ (self ):
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.', lowerCamelCase_, )
return self.image_processor
| 696 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : str = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
requires_backends(self, ['speech'] )
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
requires_backends(self, ['speech'] )
| 696 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[str] = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowerCamelCase__ : Dict = True
for i in range(0 , len(_lowerCamelCase ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
lowerCamelCase__ , lowerCamelCase__ : str = input_list[i + 1], input_list[i]
# swapping if elements not in order
lowerCamelCase__ : List[Any] = False
for i in range(1 , len(_lowerCamelCase ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
lowerCamelCase__ , lowerCamelCase__ : Tuple = input_list[i + 1], input_list[i]
# swapping if elements not in order
lowerCamelCase__ : Optional[int] = False
return input_list
if __name__ == "__main__":
print("Enter list to be sorted")
A_ : Any = [int(x) for x in input().split()]
# inputing elements of the list in one line
A_ : Tuple = odd_even_sort(input_list)
print("The sorted list is")
print(sorted_list)
| 696 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = 0
while number > 0:
lowerCamelCase__ : List[str] = number % 10
sum_of_digits += last_digit
lowerCamelCase__ : str = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def lowerCamelCase_ ( _lowerCamelCase = 100 ):
lowerCamelCase__ : Union[str, Any] = factorial(_lowerCamelCase )
lowerCamelCase__ : List[Any] = split_and_add(_lowerCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 696 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Dict = ShapEPipeline
lowerCamelCase__ : str = ['prompt']
lowerCamelCase__ : int = ['prompt']
lowerCamelCase__ : Dict = [
'num_images_per_prompt',
'num_inference_steps',
'generator',
'latents',
'guidance_scale',
'frame_size',
'output_type',
'return_dict',
]
lowerCamelCase__ : Optional[int] = False
@property
def a__ (self ):
'''simple docstring'''
return 3_2
@property
def a__ (self ):
'''simple docstring'''
return 3_2
@property
def a__ (self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def a__ (self ):
'''simple docstring'''
return 8
@property
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : List[str] = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=self.text_embedder_hidden_size, projection_dim=self.text_embedder_hidden_size, intermediate_size=3_7, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, )
return CLIPTextModelWithProjection(lowerCamelCase_ )
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Dict = {
'num_attention_heads': 2,
'attention_head_dim': 1_6,
'embedding_dim': self.time_input_dim,
'num_embeddings': 3_2,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
lowerCamelCase__ : Dict = PriorTransformer(**lowerCamelCase_ )
return model
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Union[str, Any] = {
'param_shapes': (
(self.renderer_dim, 9_3),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 1_2,
'background': (
0.1,
0.1,
0.1,
),
}
lowerCamelCase__ : List[str] = ShapERenderer(**lowerCamelCase_ )
return model
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.dummy_prior
lowerCamelCase__ : List[str] = self.dummy_text_encoder
lowerCamelCase__ : Dict = self.dummy_tokenizer
lowerCamelCase__ : List[Any] = self.dummy_renderer
lowerCamelCase__ : Optional[int] = HeunDiscreteScheduler(
beta_schedule='exp', num_train_timesteps=1_0_2_4, prediction_type='sample', use_karras_sigmas=lowerCamelCase_, clip_sample=lowerCamelCase_, clip_sample_range=1.0, )
lowerCamelCase__ : Any = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def a__ (self, lowerCamelCase_, lowerCamelCase_=0 ):
'''simple docstring'''
if str(lowerCamelCase_ ).startswith('mps' ):
lowerCamelCase__ : List[Any] = torch.manual_seed(lowerCamelCase_ )
else:
lowerCamelCase__ : Tuple = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
lowerCamelCase__ : Tuple = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 3_2,
'output_type': 'np',
}
return inputs
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = 'cpu'
lowerCamelCase__ : Tuple = self.get_dummy_components()
lowerCamelCase__ : Dict = self.pipeline_class(**lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : List[Any] = pipe(**self.get_dummy_inputs(lowerCamelCase_ ) )
lowerCamelCase__ : Optional[Any] = output.images[0]
lowerCamelCase__ : str = image[0, -3:, -3:, -1]
assert image.shape == (2_0, 3_2, 3_2, 3)
lowerCamelCase__ : Optional[int] = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def a__ (self ):
'''simple docstring'''
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = torch_device == 'cpu'
lowerCamelCase__ : Optional[int] = True
self._test_inference_batch_single_identical(
batch_size=2, test_max_difference=lowerCamelCase_, relax_max_difference=lowerCamelCase_, )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.get_dummy_components()
lowerCamelCase__ : Optional[Any] = self.pipeline_class(**lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : List[str] = 1
lowerCamelCase__ : Any = 2
lowerCamelCase__ : Optional[int] = self.get_dummy_inputs(lowerCamelCase_ )
for key in inputs.keys():
if key in self.batch_params:
lowerCamelCase__ : List[str] = batch_size * [inputs[key]]
lowerCamelCase__ : List[str] = pipe(**lowerCamelCase_, num_images_per_prompt=lowerCamelCase_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
lowerCamelCase__ : Any = ShapEPipeline.from_pretrained('openai/shap-e' )
lowerCamelCase__ : Any = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : List[Any] = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
lowerCamelCase__ : Tuple = pipe(
'a shark', generator=lowerCamelCase_, guidance_scale=15.0, num_inference_steps=6_4, frame_size=6_4, output_type='np', ).images[0]
assert images.shape == (2_0, 6_4, 6_4, 3)
assert_mean_pixel_difference(lowerCamelCase_, lowerCamelCase_ )
| 696 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A_ : Dict = "pt"
elif is_tf_available():
A_ : Union[str, Any] = "tf"
else:
A_ : List[str] = "jax"
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = PerceiverTokenizer
lowerCamelCase__ : Optional[Any] = False
def a__ (self ):
'''simple docstring'''
super().setUp()
lowerCamelCase__ : int = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def a__ (self ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver' )
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname, **lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_=False, lowerCamelCase_=2_0, lowerCamelCase_=5 ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = []
for i in range(len(lowerCamelCase_ ) ):
try:
lowerCamelCase__ : Any = tokenizer.decode([i], clean_up_tokenization_spaces=lowerCamelCase_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCamelCase__ : Any = list(filter(lambda lowerCamelCase_ : re.match(r'^[ a-zA-Z]+$', t[1] ), lowerCamelCase_ ) )
lowerCamelCase__ : Union[str, Any] = list(filter(lambda lowerCamelCase_ : [t[0]] == tokenizer.encode(t[1], add_special_tokens=lowerCamelCase_ ), lowerCamelCase_ ) )
if max_length is not None and len(lowerCamelCase_ ) > max_length:
lowerCamelCase__ : int = toks[:max_length]
if min_length is not None and len(lowerCamelCase_ ) < min_length and len(lowerCamelCase_ ) > 0:
while len(lowerCamelCase_ ) < min_length:
lowerCamelCase__ : Dict = toks + toks
# toks_str = [t[1] for t in toks]
lowerCamelCase__ : int = [t[0] for t in toks]
# Ensure consistency
lowerCamelCase__ : Optional[int] = tokenizer.decode(lowerCamelCase_, clean_up_tokenization_spaces=lowerCamelCase_ )
if " " not in output_txt and len(lowerCamelCase_ ) > 1:
lowerCamelCase__ : List[Any] = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=lowerCamelCase_ )
+ ' '
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=lowerCamelCase_ )
)
if with_prefix_space:
lowerCamelCase__ : Optional[Any] = ' ' + output_txt
lowerCamelCase__ : List[Any] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
return output_txt, output_ids
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = 'Unicode €.'
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_ )
lowerCamelCase__ : Dict = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : int = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]Unicode €.[SEP]' )
lowerCamelCase__ : List[str] = tokenizer('e è é ê ë' )
lowerCamelCase__ : Dict = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : Any = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]e è é ê ë[SEP]' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ), '[CLS]e è é ê ë[SEP]' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
lowerCamelCase__ : List[Any] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
if FRAMEWORK != "jax":
lowerCamelCase__ : List[str] = list(batch.input_ids.numpy()[0] )
else:
lowerCamelCase__ : int = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertEqual((2, 3_8), batch.input_ids.shape )
self.assertEqual((2, 3_8), batch.attention_mask.shape )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.perceiver_tokenizer
lowerCamelCase__ : List[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCamelCase__ : List[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids', lowerCamelCase_ )
self.assertIn('attention_mask', lowerCamelCase_ )
self.assertNotIn('decoder_input_ids', lowerCamelCase_ )
self.assertNotIn('decoder_attention_mask', lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : int = [
'Summary of the text.',
'Another summary.',
]
lowerCamelCase__ : str = tokenizer(
text_target=lowerCamelCase_, max_length=3_2, padding='max_length', truncation=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertEqual(3_2, targets['input_ids'].shape[1] )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length, 4_2 )
# Now let's start the test
lowerCamelCase__ : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : str = ' He is very happy, UNwant\u00E9d,running'
lowerCamelCase__ : str = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : str = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
shutil.rmtree(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : Union[str, Any] = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
lowerCamelCase__ : List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
lowerCamelCase__ : List[str] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : int = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Tuple = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertIn('new_additional_special_token', after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length, 4_2 )
lowerCamelCase__ : List[Any] = tokenizer.__class__.from_pretrained(lowerCamelCase_, model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length, 4_3 )
shutil.rmtree(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : Optional[Any] = json.load(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : List[str] = json.load(lowerCamelCase_ )
lowerCamelCase__ : Any = [f'''<extra_id_{i}>''' for i in range(1_2_5 )]
lowerCamelCase__ : Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
lowerCamelCase__ : List[str] = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCamelCase__ : Dict = tokenizer_class.from_pretrained(
lowerCamelCase_, )
self.assertIn(
'an_additional_special_token', tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['an_additional_special_token'], tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ), )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCamelCase__ : Optional[Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token', lstrip=lowerCamelCase_ )]
lowerCamelCase__ : Any = tokenizer_class.from_pretrained(
lowerCamelCase_, additional_special_tokens=lowerCamelCase_, )
self.assertIn('a_new_additional_special_token', tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'], tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ), )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ), '�' )
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.get_tokenizers(fast=lowerCamelCase_, do_lower_case=lowerCamelCase_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase__ : Tuple = ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
lowerCamelCase__ : List[str] = tokenizer.convert_tokens_to_string(lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__name__)
A_ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = 'megatron-bert'
def __init__(self, lowerCamelCase_=2_9_0_5_6, lowerCamelCase_=1_0_2_4, lowerCamelCase_=2_4, lowerCamelCase_=1_6, lowerCamelCase_=4_0_9_6, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=1e-12, lowerCamelCase_=0, lowerCamelCase_="absolute", lowerCamelCase_=True, **lowerCamelCase_, ):
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase_, **lowerCamelCase_ )
lowerCamelCase__ : List[Any] = vocab_size
lowerCamelCase__ : Any = hidden_size
lowerCamelCase__ : str = num_hidden_layers
lowerCamelCase__ : List[str] = num_attention_heads
lowerCamelCase__ : Tuple = hidden_act
lowerCamelCase__ : Optional[int] = intermediate_size
lowerCamelCase__ : List[Any] = hidden_dropout_prob
lowerCamelCase__ : Any = attention_probs_dropout_prob
lowerCamelCase__ : List[str] = max_position_embeddings
lowerCamelCase__ : Dict = type_vocab_size
lowerCamelCase__ : List[Any] = initializer_range
lowerCamelCase__ : Union[str, Any] = layer_norm_eps
lowerCamelCase__ : Optional[Any] = position_embedding_type
lowerCamelCase__ : Optional[int] = use_cache
| 696 |
"""simple docstring"""
from math import pi, sqrt, tan
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
lowerCamelCase__ : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_lowerCamelCase , 2 ) * torus_radius * tube_radius
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
lowerCamelCase__ : Dict = (sidea + sidea + sidea) / 2
lowerCamelCase__ : str = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 696 | 1 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
lowerCamelCase__ : str = flax_key_tuple[:-1] + ('weight',)
lowerCamelCase__ : int = torch.permute(_lowerCamelCase , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase ):
# linear layer
lowerCamelCase__ : str = flax_key_tuple[:-1] + ('weight',)
lowerCamelCase__ : List[Any] = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowerCamelCase__ : Dict = flax_key_tuple[:-1] + ('weight',)
return flax_key_tuple, flax_tensor
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if "metadata" in layer:
lowerCamelCase__ : List[Any] = layer.split('metadata' )
lowerCamelCase__ : List[Any] = ''.join(split_layer[0] )[:-1]
lowerCamelCase__ : Tuple = [tuple(('metadata' + split_layer[1]).split('/' ) )]
elif "kvstore" in layer:
lowerCamelCase__ : Optional[Any] = layer.split('kvstore' )
lowerCamelCase__ : List[Any] = ''.join(split_layer[0] )[:-1]
lowerCamelCase__ : List[Any] = [tuple(('kvstore' + split_layer[1]).split('/' ) )]
else:
lowerCamelCase__ : Tuple = layer.split('/' )
lowerCamelCase__ : Dict = '/'.join(split_layer[:-1] )
lowerCamelCase__ : str = (split_layer[-1],)
if "kvstore/path" in layer:
lowerCamelCase__ : str = f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
elif "kvstore/driver" in layer:
lowerCamelCase__ : str = 'file'
else:
lowerCamelCase__ : str = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Tuple = rename_keys(_lowerCamelCase )
lowerCamelCase__ : List[str] = {}
for k, v in current_block.items():
lowerCamelCase__ : List[str] = v
lowerCamelCase__ : Optional[Any] = new_current_block
torch.save(_lowerCamelCase , _lowerCamelCase )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = WEIGHTS_NAME ):
lowerCamelCase__ : List[Any] = convert_file_size_to_int(_lowerCamelCase )
lowerCamelCase__ : int = []
lowerCamelCase__ : Any = {}
lowerCamelCase__ : Union[str, Any] = 0
lowerCamelCase__ : Tuple = 0
os.makedirs(_lowerCamelCase , exist_ok=_lowerCamelCase )
with gfile.GFile(switch_checkpoint_path + '/checkpoint' , 'rb' ) as fp:
lowerCamelCase__ : Optional[int] = serialization.msgpack_restore(fp.read() )['optimizer']['target']
lowerCamelCase__ : Optional[int] = flatten_dict(_lowerCamelCase , sep='/' )
lowerCamelCase__ : Dict = {}
for layer in checkpoint_info.keys():
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = get_key_and_tensorstore_dict(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if curr_real_layer_name in all_layers:
lowerCamelCase__ : Union[str, Any] = content
else:
lowerCamelCase__ : int = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
lowerCamelCase__ : List[str] = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
lowerCamelCase__ : int = torch.tensor(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = rename_base_flax_keys(tuple(key.split('/' ) ) , _lowerCamelCase )
lowerCamelCase__ : List[Any] = '/'.join(_lowerCamelCase )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
lowerCamelCase__ : Any = os.path.join(
_lowerCamelCase , weights_name.replace('.bin' , f'''-{len(_lowerCamelCase )+1:05d}-of-???.bin''' ) )
rename_and_save_block(_lowerCamelCase , _lowerCamelCase )
sharded_state_dicts.append(current_block.keys() )
del current_block
lowerCamelCase__ : List[str] = {}
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Optional[Any] = raw_weights.to(getattr(_lowerCamelCase , _lowerCamelCase ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
lowerCamelCase__ : Any = os.path.join(_lowerCamelCase , weights_name.replace('.bin' , f'''-{len(_lowerCamelCase )+1:05d}-of-???.bin''' ) )
rename_and_save_block(_lowerCamelCase , _lowerCamelCase )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(_lowerCamelCase ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
lowerCamelCase__ : Tuple = {}
lowerCamelCase__ : str = {}
for idx, shard in enumerate(_lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = weights_name.replace(
'.bin' , f'''-{idx+1:05d}-of-{len(_lowerCamelCase ):05d}.bin''' ) # len(sharded_state_dicts):05d}
lowerCamelCase__ : int = os.path.join(_lowerCamelCase , weights_name.replace('.bin' , f'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(_lowerCamelCase , os.path.join(_lowerCamelCase , _lowerCamelCase ) )
lowerCamelCase__ : str = shard
for key in shard:
lowerCamelCase__ : Tuple = shard_file
# Add the metadata
lowerCamelCase__ : Union[str, Any] = {'total_size': total_size}
lowerCamelCase__ : Dict = {'metadata': metadata, 'weight_map': weight_map}
with open(os.path.join(_lowerCamelCase , _lowerCamelCase ) , 'w' , encoding='utf-8' ) as f:
lowerCamelCase__ : Any = json.dumps(_lowerCamelCase , indent=2 , sort_keys=_lowerCamelCase ) + '\n'
f.write(_lowerCamelCase )
return metadata, index
if __name__ == "__main__":
A_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
type=str,
required=False,
help="Path to the output pytorch model.",
)
A_ : List[Any] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowerCamelCase_ ( ):
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
lowerCamelCase__ : List[str] = SwitchTransformersConfig.from_pretrained('google/switch-base-8' )
config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted' )
lowerCamelCase__ : Tuple = SwitchTransformersForConditionalGeneration.from_pretrained(
'/home/arthur_huggingface_co/transformers/switch_converted' , device_map='auto' )
lowerCamelCase__ : Optional[int] = TaTokenizer.from_pretrained('t5-small' )
lowerCamelCase__ : Optional[int] = 'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'
lowerCamelCase__ : str = tokenizer(_lowerCamelCase , return_tensors='pt' ).input_ids
lowerCamelCase__ : Tuple = model.generate(_lowerCamelCase , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 696 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=9_9, lowerCamelCase_=6_4, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Dict = parent
lowerCamelCase__ : Tuple = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : List[Any] = is_training
lowerCamelCase__ : str = use_input_mask
lowerCamelCase__ : Optional[Any] = use_token_type_ids
lowerCamelCase__ : Any = use_labels
lowerCamelCase__ : Optional[int] = vocab_size
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : Optional[int] = num_hidden_layers
lowerCamelCase__ : List[Any] = num_attention_heads
lowerCamelCase__ : Union[str, Any] = intermediate_size
lowerCamelCase__ : List[str] = hidden_act
lowerCamelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCamelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCamelCase__ : Dict = max_position_embeddings
lowerCamelCase__ : Dict = type_vocab_size
lowerCamelCase__ : Union[str, Any] = type_sequence_label_size
lowerCamelCase__ : List[Any] = initializer_range
lowerCamelCase__ : List[Any] = num_labels
lowerCamelCase__ : Union[str, Any] = num_choices
lowerCamelCase__ : List[str] = scope
lowerCamelCase__ : Dict = vocab_size - 1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Optional[Any] = None
if self.use_input_mask:
lowerCamelCase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Any = None
if self.use_labels:
lowerCamelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : str = self.get_config()
return config, input_ids, input_mask, token_labels
def a__ (self ):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=lowerCamelCase_, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[str] = self.prepare_config_and_inputs()
lowerCamelCase__ : Optional[Any] = True
return config, input_ids, input_mask, token_labels
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = GPTNeoXModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.num_labels
lowerCamelCase__ : Optional[Any] = GPTNeoXForQuestionAnswering(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : str = self.num_labels
lowerCamelCase__ : Optional[int] = GPTNeoXForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : List[Any] = GPTNeoXForTokenClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Tuple = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = True
lowerCamelCase__ : List[str] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
# first forward pass
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, use_cache=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase__ : str = ids_tensor((self.batch_size, 3), config.vocab_size )
lowerCamelCase__ : List[Any] = ids_tensor((self.batch_size, 3), vocab_size=2 )
# append to next input_ids and
lowerCamelCase__ : Tuple = torch.cat([input_ids, next_tokens], dim=-1 )
lowerCamelCase__ : Tuple = torch.cat([input_mask, next_mask], dim=-1 )
lowerCamelCase__ : List[str] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, output_hidden_states=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = output_from_no_past['hidden_states'][0]
lowerCamelCase__ : Optional[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, past_key_values=lowerCamelCase_, output_hidden_states=lowerCamelCase_, )['hidden_states'][0]
# select random slice
lowerCamelCase__ : Dict = ids_tensor((1,), output_from_past.shape[-1] ).item()
lowerCamelCase__ : Optional[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-3 ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = config_and_inputs
lowerCamelCase__ : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : int = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ : Dict = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Optional[int] = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = GPTNeoXModelTester(self )
lowerCamelCase__ : Union[str, Any] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=6_4, num_attention_heads=8 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCamelCase__ : Optional[Any] = None
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
@unittest.skip(reason='Feed forward chunking is not implemented' )
def a__ (self ):
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Optional[Any] = ids_tensor([1, 1_0], config.vocab_size )
lowerCamelCase__ : Tuple = ids_tensor([1, int(config.max_position_embeddings * 1.5 )], config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Any = GPTNeoXModel(lowerCamelCase_ )
original_model.to(lowerCamelCase_ )
original_model.eval()
lowerCamelCase__ : List[Any] = original_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = original_model(lowerCamelCase_ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Optional[int] = {'type': scaling_type, 'factor': 10.0}
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
scaled_model.to(lowerCamelCase_ )
scaled_model.eval()
lowerCamelCase__ : Tuple = scaled_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = scaled_model(lowerCamelCase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
@require_torch
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' )
for checkpointing in [True, False]:
lowerCamelCase__ : Optional[Any] = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = tokenizer('My favorite food is', return_tensors='pt' ).to(lowerCamelCase_ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
lowerCamelCase__ : Dict = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
lowerCamelCase__ : Dict = model.generate(**lowerCamelCase_, do_sample=lowerCamelCase_, max_new_tokens=2_0 )
lowerCamelCase__ : Optional[Any] = tokenizer.batch_decode(lowerCamelCase_ )[0]
self.assertEqual(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
from math import pi, sqrt, tan
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
lowerCamelCase__ : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_lowerCamelCase , 2 ) * torus_radius * tube_radius
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
lowerCamelCase__ : Dict = (sidea + sidea + sidea) / 2
lowerCamelCase__ : str = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 696 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A_ : Dict = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
A_ : List[Any] = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
A_ : Union[str, Any] = spec.loader.load_module()
A_ : int = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
A_ : Optional[int] = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
A_ : str = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def lowerCamelCase_ ( ):
lowerCamelCase__ : Dict = []
for config_class in list(CONFIG_MAPPING.values() ):
lowerCamelCase__ : Dict = False
# source code of `config_class`
lowerCamelCase__ : str = inspect.getsource(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = _re_checkpoint.findall(_lowerCamelCase )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
lowerCamelCase__ : Any = f'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
lowerCamelCase__ : Any = True
break
lowerCamelCase__ : Dict = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
lowerCamelCase__ : Optional[Any] = '\n'.join(sorted(_lowerCamelCase ) )
raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 696 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : Dict = logging.get_logger(__name__)
A_ : Optional[Any] = {
"google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class a_ ( snake_case_ , snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = 'bit'
lowerCamelCase__ : Union[str, Any] = ['preactivation', 'bottleneck']
lowerCamelCase__ : List[Any] = ['SAME', 'VALID']
def __init__(self, lowerCamelCase_=3, lowerCamelCase_=6_4, lowerCamelCase_=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8], lowerCamelCase_=[3, 4, 6, 3], lowerCamelCase_="preactivation", lowerCamelCase_="relu", lowerCamelCase_=None, lowerCamelCase_=3_2, lowerCamelCase_=0.0, lowerCamelCase_=False, lowerCamelCase_=3_2, lowerCamelCase_=1, lowerCamelCase_=None, lowerCamelCase_=None, **lowerCamelCase_, ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
if layer_type not in self.layer_types:
raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
lowerCamelCase__ : Optional[int] = global_padding.upper()
else:
raise ValueError(f'''Padding strategy {global_padding} not supported''' )
lowerCamelCase__ : Optional[Any] = num_channels
lowerCamelCase__ : Any = embedding_size
lowerCamelCase__ : Union[str, Any] = hidden_sizes
lowerCamelCase__ : Optional[Any] = depths
lowerCamelCase__ : List[str] = layer_type
lowerCamelCase__ : Optional[Any] = hidden_act
lowerCamelCase__ : Union[str, Any] = global_padding
lowerCamelCase__ : Optional[int] = num_groups
lowerCamelCase__ : List[Any] = drop_path_rate
lowerCamelCase__ : List[str] = embedding_dynamic_padding
lowerCamelCase__ : Union[str, Any] = output_stride
lowerCamelCase__ : Dict = width_factor
lowerCamelCase__ : Union[str, Any] = ['stem'] + [f'''stage{idx}''' for idx in range(1, len(lowerCamelCase_ ) + 1 )]
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = get_aligned_output_features_output_indices(
out_features=lowerCamelCase_, out_indices=lowerCamelCase_, stage_names=self.stage_names )
| 696 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = ["LlamaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = ["LlamaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"LlamaForCausalLM",
"LlamaModel",
"LlamaPreTrainedModel",
"LlamaForSequenceClassification",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
A_ : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase = 100 ):
lowerCamelCase__ : int = 0
lowerCamelCase__ : Optional[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 696 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A_ : Optional[int] = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A_ : List[str] = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
for data in res.iter_content(1_00_00):
out_file.write(data)
A_ : Tuple = BeautifulSoup(res.text, "html.parser")
A_ : Dict = list(soup.select(".eZt8xd"))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("href"))
else:
webbrowser.open(f"https://google.com{link.get('href')}")
| 696 | 1 |
"""simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = torch.exp(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = torch.sum(_lowerCamelCase , dim=1 ) # sum of exp(x_i)
lowerCamelCase__ : Any = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(_lowerCamelCase ) - B / A
class a_ ( nn.Module ):
'''simple docstring'''
def __init__(self, lowerCamelCase_ ):
'''simple docstring'''
super().__init__()
lowerCamelCase__ : int = config.output_attentions
lowerCamelCase__ : str = config.output_hidden_states
lowerCamelCase__ : List[Any] = nn.ModuleList([BertLayer(lowerCamelCase_ ) for _ in range(config.num_hidden_layers )] )
lowerCamelCase__ : Dict = nn.ModuleList([BertHighway(lowerCamelCase_ ) for _ in range(config.num_hidden_layers )] )
lowerCamelCase__ : str = [-1 for _ in range(config.num_hidden_layers )]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
if (type(lowerCamelCase_ ) is float) or (type(lowerCamelCase_ ) is int):
for i in range(len(self.early_exit_entropy ) ):
lowerCamelCase__ : str = x
else:
lowerCamelCase__ : str = x
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def a__ (self, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Dict = ()
lowerCamelCase__ : int = ()
lowerCamelCase__ : Dict = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
lowerCamelCase__ : Tuple = all_hidden_states + (hidden_states,)
lowerCamelCase__ : Any = layer_module(
lowerCamelCase_, lowerCamelCase_, head_mask[i], lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : str = layer_outputs[0]
if self.output_attentions:
lowerCamelCase__ : int = all_attentions + (layer_outputs[1],)
lowerCamelCase__ : Union[str, Any] = (hidden_states,)
if self.output_hidden_states:
lowerCamelCase__ : Optional[Any] = current_outputs + (all_hidden_states,)
if self.output_attentions:
lowerCamelCase__ : str = current_outputs + (all_attentions,)
lowerCamelCase__ : str = self.highway[i](lowerCamelCase_ )
# logits, pooled_output
if not self.training:
lowerCamelCase__ : Optional[int] = highway_exit[0]
lowerCamelCase__ : List[str] = entropy(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
lowerCamelCase__ : List[str] = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
lowerCamelCase__ : Tuple = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(lowerCamelCase_, i + 1 )
else:
lowerCamelCase__ : Tuple = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
lowerCamelCase__ : int = all_hidden_states + (hidden_states,)
lowerCamelCase__ : Optional[Any] = (hidden_states,)
if self.output_hidden_states:
lowerCamelCase__ : Any = outputs + (all_hidden_states,)
if self.output_attentions:
lowerCamelCase__ : int = outputs + (all_attentions,)
lowerCamelCase__ : Optional[Any] = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
'The Bert Model transformer with early exiting (DeeBERT). ' , snake_case_ , )
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_ ):
'''simple docstring'''
super().__init__(lowerCamelCase_ )
lowerCamelCase__ : str = config
lowerCamelCase__ : Dict = BertEmbeddings(lowerCamelCase_ )
lowerCamelCase__ : Any = DeeBertEncoder(lowerCamelCase_ )
lowerCamelCase__ : Tuple = BertPooler(lowerCamelCase_ )
self.init_weights()
def a__ (self ):
'''simple docstring'''
self.encoder.init_highway_pooler(self.pooler )
def a__ (self ):
'''simple docstring'''
return self.embeddings.word_embeddings
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = value
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(lowerCamelCase_ )
@add_start_docstrings_to_model_forward(lowerCamelCase_ )
def a__ (self, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, ):
'''simple docstring'''
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
lowerCamelCase__ : Optional[Any] = input_ids.size()
elif inputs_embeds is not None:
lowerCamelCase__ : int = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
lowerCamelCase__ : Dict = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowerCamelCase__ : str = torch.ones(lowerCamelCase_, device=lowerCamelCase_ )
if encoder_attention_mask is None:
lowerCamelCase__ : Any = torch.ones(lowerCamelCase_, device=lowerCamelCase_ )
if token_type_ids is None:
lowerCamelCase__ : Tuple = torch.zeros(lowerCamelCase_, dtype=torch.long, device=lowerCamelCase_ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowerCamelCase__ : torch.Tensor = self.get_extended_attention_mask(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
lowerCamelCase__ : List[str] = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
lowerCamelCase__ : int = encoder_attention_mask[:, None, None, :]
lowerCamelCase__ : Tuple = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
lowerCamelCase__ : Union[str, Any] = (1.0 - encoder_extended_attention_mask) * -10_000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowerCamelCase__ : Optional[int] = self.get_head_mask(lowerCamelCase_, self.config.num_hidden_layers )
lowerCamelCase__ : Tuple = self.embeddings(
input_ids=lowerCamelCase_, position_ids=lowerCamelCase_, token_type_ids=lowerCamelCase_, inputs_embeds=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = self.encoder(
lowerCamelCase_, attention_mask=lowerCamelCase_, head_mask=lowerCamelCase_, encoder_hidden_states=lowerCamelCase_, encoder_attention_mask=lowerCamelCase_, )
lowerCamelCase__ : List[str] = encoder_outputs[0]
lowerCamelCase__ : Tuple = self.pooler(lowerCamelCase_ )
lowerCamelCase__ : List[str] = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : int = message
lowerCamelCase__ : Tuple = exit_layer # start from 1!
class a_ ( nn.Module ):
'''simple docstring'''
def __init__(self, lowerCamelCase_ ):
'''simple docstring'''
super().__init__()
lowerCamelCase__ : Optional[Any] = BertPooler(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = nn.Dropout(config.hidden_dropout_prob )
lowerCamelCase__ : Optional[Any] = nn.Linear(config.hidden_size, config.num_labels )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = encoder_outputs[0]
lowerCamelCase__ : Union[str, Any] = self.pooler(lowerCamelCase_ )
# "return" pooler_output
# BertModel
lowerCamelCase__ : Optional[Any] = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
lowerCamelCase__ : List[Any] = bmodel_output[1]
lowerCamelCase__ : str = self.dropout(lowerCamelCase_ )
lowerCamelCase__ : Tuple = self.classifier(lowerCamelCase_ )
return logits, pooled_output
@add_start_docstrings(
'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n also takes care of multi-layer training. ' , snake_case_ , )
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_ ):
'''simple docstring'''
super().__init__(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = config.num_labels
lowerCamelCase__ : Dict = config.num_hidden_layers
lowerCamelCase__ : Any = DeeBertModel(lowerCamelCase_ )
lowerCamelCase__ : Dict = nn.Dropout(config.hidden_dropout_prob )
lowerCamelCase__ : List[Any] = nn.Linear(config.hidden_size, self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCamelCase_ )
def a__ (self, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=-1, lowerCamelCase_=False, ):
'''simple docstring'''
lowerCamelCase__ : List[str] = self.num_layers
try:
lowerCamelCase__ : int = self.bert(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, position_ids=lowerCamelCase_, head_mask=lowerCamelCase_, inputs_embeds=lowerCamelCase_, )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
lowerCamelCase__ : Optional[Any] = outputs[1]
lowerCamelCase__ : List[Any] = self.dropout(lowerCamelCase_ )
lowerCamelCase__ : Dict = self.classifier(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
lowerCamelCase__ : int = e.message
lowerCamelCase__ : List[Any] = e.exit_layer
lowerCamelCase__ : Optional[int] = outputs[0]
if not self.training:
lowerCamelCase__ : Dict = entropy(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = []
lowerCamelCase__ : Optional[int] = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
lowerCamelCase__ : List[str] = MSELoss()
lowerCamelCase__ : Union[str, Any] = loss_fct(logits.view(-1 ), labels.view(-1 ) )
else:
lowerCamelCase__ : List[Any] = CrossEntropyLoss()
lowerCamelCase__ : Union[str, Any] = loss_fct(logits.view(-1, self.num_labels ), labels.view(-1 ) )
# work with highway exits
lowerCamelCase__ : Optional[int] = []
for highway_exit in outputs[-1]:
lowerCamelCase__ : str = highway_exit[0]
if not self.training:
highway_logits_all.append(lowerCamelCase_ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
lowerCamelCase__ : Union[str, Any] = MSELoss()
lowerCamelCase__ : Any = loss_fct(highway_logits.view(-1 ), labels.view(-1 ) )
else:
lowerCamelCase__ : Tuple = CrossEntropyLoss()
lowerCamelCase__ : Optional[Any] = loss_fct(highway_logits.view(-1, self.num_labels ), labels.view(-1 ) )
highway_losses.append(lowerCamelCase_ )
if train_highway:
lowerCamelCase__ : Dict = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
lowerCamelCase__ : Tuple = (loss,) + outputs
if not self.training:
lowerCamelCase__ : Any = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
lowerCamelCase__ : Optional[Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 696 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
lowerCamelCase__ : Tuple = FlaxDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=lowerCamelCase_, cache_dir=lowerCamelCase_ )
lowerCamelCase__ : List[str] = [t[-1] for t in os.walk(os.path.join(lowerCamelCase_, os.listdir(lowerCamelCase_ )[0], 'snapshots' ) )]
lowerCamelCase__ : Optional[int] = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('.bin' ) for f in files )
@slow
@require_flax
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Any = FlaxStableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Any = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Optional[int] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Any = 4
lowerCamelCase__ : Any = jax.device_count()
lowerCamelCase__ : List[Any] = num_samples * [prompt]
lowerCamelCase__ : Optional[int] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : int = replicate(lowerCamelCase_ )
lowerCamelCase__ : Any = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : int = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 4.1_514_745 ) < 1e-3
assert np.abs(np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 49_947.875 ) < 5e-1
lowerCamelCase__ : Union[str, Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(lowerCamelCase_ ) == num_samples
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='flax', safety_checker=lowerCamelCase_ )
lowerCamelCase__ : int = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : List[str] = jax.random.PRNGKey(0 )
lowerCamelCase__ : int = 5_0
lowerCamelCase__ : List[str] = jax.device_count()
lowerCamelCase__ : Dict = num_samples * [prompt]
lowerCamelCase__ : List[str] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Dict = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = shard(lowerCamelCase_ )
lowerCamelCase__ : str = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.05_652_401) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_383_808.2) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : List[Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Union[str, Any] = 5_0
lowerCamelCase__ : Any = jax.device_count()
lowerCamelCase__ : Tuple = num_samples * [prompt]
lowerCamelCase__ : List[str] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Any = replicate(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : int = shard(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.04_003_906) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_373_516.75) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Tuple = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa )
lowerCamelCase__ : Tuple = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Union[str, Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Optional[Any] = 5_0
lowerCamelCase__ : Tuple = jax.device_count()
lowerCamelCase__ : Optional[int] = num_samples * [prompt]
lowerCamelCase__ : str = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Optional[int] = replicate(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : List[str] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.04_003_906) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_373_516.75) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = FlaxDDIMScheduler(
beta_start=0.00_085, beta_end=0.012, beta_schedule='scaled_linear', set_alpha_to_one=lowerCamelCase_, steps_offset=1, )
lowerCamelCase__ , lowerCamelCase__ : List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, scheduler=lowerCamelCase_, safety_checker=lowerCamelCase_, )
lowerCamelCase__ : List[str] = scheduler.create_state()
lowerCamelCase__ : int = scheduler_state
lowerCamelCase__ : Any = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Optional[Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : int = 5_0
lowerCamelCase__ : Optional[Any] = jax.device_count()
lowerCamelCase__ : Any = num_samples * [prompt]
lowerCamelCase__ : Any = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Union[str, Any] = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Dict = shard(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.045_043_945) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_347_693.5) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : int = jax.device_count()
lowerCamelCase__ : Dict = num_samples * [prompt]
lowerCamelCase__ : str = jax.random.split(jax.random.PRNGKey(0 ), lowerCamelCase_ )
lowerCamelCase__ , lowerCamelCase__ : List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_, )
lowerCamelCase__ : Union[str, Any] = replicate(lowerCamelCase_ )
lowerCamelCase__ : Dict = pipeline.prepare_inputs(lowerCamelCase_ )
lowerCamelCase__ : Tuple = shard(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : int = images[2, 0, 2_5_6, 1_0:1_7, 1]
# With memory efficient attention
lowerCamelCase__ , lowerCamelCase__ : str = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_, use_memory_efficient_attention=lowerCamelCase_, )
lowerCamelCase__ : Dict = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = pipeline.prepare_inputs(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : Any = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : Any = images[2, 0, 2_5_6, 1_0:1_7, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1e-2
| 696 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : Any = logging.get_logger(__name__)
A_ : Tuple = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class a_ ( snake_case_ , snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = 'swin'
lowerCamelCase__ : Any = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__(self, lowerCamelCase_=2_2_4, lowerCamelCase_=4, lowerCamelCase_=3, lowerCamelCase_=9_6, lowerCamelCase_=[2, 2, 6, 2], lowerCamelCase_=[3, 6, 1_2, 2_4], lowerCamelCase_=7, lowerCamelCase_=4.0, lowerCamelCase_=True, lowerCamelCase_=0.0, lowerCamelCase_=0.0, lowerCamelCase_=0.1, lowerCamelCase_="gelu", lowerCamelCase_=False, lowerCamelCase_=0.02, lowerCamelCase_=1e-5, lowerCamelCase_=3_2, lowerCamelCase_=None, lowerCamelCase_=None, **lowerCamelCase_, ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
lowerCamelCase__ : List[Any] = image_size
lowerCamelCase__ : Tuple = patch_size
lowerCamelCase__ : Optional[int] = num_channels
lowerCamelCase__ : str = embed_dim
lowerCamelCase__ : Union[str, Any] = depths
lowerCamelCase__ : List[Any] = len(lowerCamelCase_ )
lowerCamelCase__ : List[str] = num_heads
lowerCamelCase__ : Tuple = window_size
lowerCamelCase__ : List[Any] = mlp_ratio
lowerCamelCase__ : Optional[int] = qkv_bias
lowerCamelCase__ : List[str] = hidden_dropout_prob
lowerCamelCase__ : Any = attention_probs_dropout_prob
lowerCamelCase__ : Optional[int] = drop_path_rate
lowerCamelCase__ : Any = hidden_act
lowerCamelCase__ : List[Any] = use_absolute_embeddings
lowerCamelCase__ : List[str] = layer_norm_eps
lowerCamelCase__ : int = initializer_range
lowerCamelCase__ : int = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase__ : Dict = int(embed_dim * 2 ** (len(lowerCamelCase_ ) - 1) )
lowerCamelCase__ : str = ['stem'] + [f'''stage{idx}''' for idx in range(1, len(lowerCamelCase_ ) + 1 )]
lowerCamelCase__ , lowerCamelCase__ : List[Any] = get_aligned_output_features_output_indices(
out_features=lowerCamelCase_, out_indices=lowerCamelCase_, stage_names=self.stage_names )
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : str = version.parse('1.11' )
@property
def a__ (self ):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def a__ (self ):
'''simple docstring'''
return 1e-4
| 696 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A_ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowerCamelCase_, scheduler=lowerCamelCase_ )
@torch.no_grad()
def __call__(self, lowerCamelCase_ = 1, lowerCamelCase_ = 1_0_0, lowerCamelCase_ = None, lowerCamelCase_ = None, lowerCamelCase_ = True, ):
'''simple docstring'''
if audio_length_in_s is None:
lowerCamelCase__ : str = self.unet.config.sample_size / self.unet.config.sample_rate
lowerCamelCase__ : Optional[Any] = audio_length_in_s * self.unet.config.sample_rate
lowerCamelCase__ : str = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowerCamelCase__ : Dict = int(lowerCamelCase_ )
if sample_size % down_scale_factor != 0:
lowerCamelCase__ : Union[str, Any] = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
' process.' )
lowerCamelCase__ : Optional[Any] = int(lowerCamelCase_ )
lowerCamelCase__ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowerCamelCase__ : Union[str, Any] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowerCamelCase_, lowerCamelCase_ ) and len(lowerCamelCase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowerCamelCase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowerCamelCase__ : Union[str, Any] = randn_tensor(lowerCamelCase_, generator=lowerCamelCase_, device=self.device, dtype=lowerCamelCase_ )
# set step values
self.scheduler.set_timesteps(lowerCamelCase_, device=audio.device )
lowerCamelCase__ : int = self.scheduler.timesteps.to(lowerCamelCase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowerCamelCase__ : List[Any] = self.unet(lowerCamelCase_, lowerCamelCase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowerCamelCase__ : List[str] = self.scheduler.step(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ).prev_sample
lowerCamelCase__ : Union[str, Any] = audio.clamp(-1, 1 ).float().cpu().numpy()
lowerCamelCase__ : Tuple = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
A_ : Any = "__DUMMY_TRANSFORMERS_USER__"
A_ : Tuple = "Dummy User"
A_ : str = "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"
A_ : List[str] = "https://hub-ci.huggingface.co"
A_ : Optional[int] = CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
A_ : int = CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
A_ : Any = Path("~/.huggingface/hub_ci_token").expanduser()
@pytest.fixture
def lowerCamelCase_ ( _lowerCamelCase ):
monkeypatch.setattr(
'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE' , _lowerCamelCase )
@pytest.fixture
def lowerCamelCase_ ( _lowerCamelCase ):
monkeypatch.setattr('datasets.config.HF_ENDPOINT' , _lowerCamelCase )
monkeypatch.setattr('datasets.config.HUB_DATASETS_URL' , _lowerCamelCase )
@pytest.fixture
def lowerCamelCase_ ( _lowerCamelCase ):
monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token' , _lowerCamelCase )
@pytest.fixture
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
HfFolder.save_token(_lowerCamelCase )
yield
HfFolder.delete_token()
@pytest.fixture(scope='session' )
def lowerCamelCase_ ( ):
return HfApi(endpoint=_lowerCamelCase )
@pytest.fixture(scope='session' )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[int] = HfFolder.get_token()
HfFolder.save_token(_lowerCamelCase )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(_lowerCamelCase )
@pytest.fixture
def lowerCamelCase_ ( _lowerCamelCase ):
def _cleanup_repo(_lowerCamelCase ):
hf_api.delete_repo(_lowerCamelCase , token=_lowerCamelCase , repo_type='dataset' )
return _cleanup_repo
@pytest.fixture
def lowerCamelCase_ ( _lowerCamelCase ):
@contextmanager
def _temporary_repo(_lowerCamelCase ):
try:
yield repo_id
finally:
cleanup_repo(_lowerCamelCase )
return _temporary_repo
@pytest.fixture(scope='session' )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Optional[int] = f'''repo_txt_data-{int(time.time() * 10e3 )}'''
lowerCamelCase__ : Any = f'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(_lowerCamelCase , token=_lowerCamelCase , repo_type='dataset' , private=_lowerCamelCase )
hf_api.upload_file(
token=_lowerCamelCase , path_or_fileobj=str(_lowerCamelCase ) , path_in_repo='data/text_data.txt' , repo_id=_lowerCamelCase , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(_lowerCamelCase , token=_lowerCamelCase , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope='session' )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Any = f'''repo_zipped_txt_data-{int(time.time() * 10e3 )}'''
lowerCamelCase__ : int = f'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(_lowerCamelCase , token=_lowerCamelCase , repo_type='dataset' , private=_lowerCamelCase )
hf_api.upload_file(
token=_lowerCamelCase , path_or_fileobj=str(_lowerCamelCase ) , path_in_repo='data.zip' , repo_id=_lowerCamelCase , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(_lowerCamelCase , token=_lowerCamelCase , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope='session' )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Optional[int] = f'''repo_zipped_img_data-{int(time.time() * 10e3 )}'''
lowerCamelCase__ : Union[str, Any] = f'''{CI_HUB_USER}/{repo_name}'''
hf_api.create_repo(_lowerCamelCase , token=_lowerCamelCase , repo_type='dataset' , private=_lowerCamelCase )
hf_api.upload_file(
token=_lowerCamelCase , path_or_fileobj=str(_lowerCamelCase ) , path_in_repo='data.zip' , repo_id=_lowerCamelCase , repo_type='dataset' , )
yield repo_id
try:
hf_api.delete_repo(_lowerCamelCase , token=_lowerCamelCase , repo_type='dataset' )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
return hf_private_dataset_repo_zipped_img_data_
| 696 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class a_ :
'''simple docstring'''
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return None
class a_ :
'''simple docstring'''
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return None
class a_ ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase_, 'tf', 1_2, **lowerCamelCase_ )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase_, 'pt', 1_2, **lowerCamelCase_ )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
from transformers import BertModel
lowerCamelCase__ : Union[str, Any] = ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
with NamedTemporaryFile(mode='w+t' ) as vocab_file:
vocab_file.write('\n'.join(lowerCamelCase_ ) )
vocab_file.flush()
lowerCamelCase__ : Tuple = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowerCamelCase__ : Optional[Any] = BertModel(BertConfig(vocab_size=len(lowerCamelCase_ ) ) )
model.save_pretrained(lowerCamelCase_ )
self._test_export(lowerCamelCase_, 'pt', 1_2, lowerCamelCase_ )
@require_tf
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCamelCase__ : Optional[Any] = self._test_export(lowerCamelCase_, 'tf', 1_2, **lowerCamelCase_ )
lowerCamelCase__ : Any = quantize(Path(lowerCamelCase_ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase_ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCamelCase__ : Any = self._test_export(lowerCamelCase_, 'pt', 1_2, **lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = quantize(lowerCamelCase_ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase_ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, **lowerCamelCase_ ):
'''simple docstring'''
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowerCamelCase__ : str = Path(lowerCamelCase_ ).joinpath('model.onnx' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ )
return path
except Exception as e:
self.fail(lowerCamelCase_ )
@require_torch
@require_tokenizers
@slow
def a__ (self ):
'''simple docstring'''
from transformers import BertModel
lowerCamelCase__ : str = BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowerCamelCase__ : Union[str, Any] = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(lowerCamelCase_, lowerCamelCase_, 'pt' )
@require_tf
@require_tokenizers
@slow
def a__ (self ):
'''simple docstring'''
from transformers import TFBertModel
lowerCamelCase__ : Dict = TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowerCamelCase__ : Optional[int] = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(lowerCamelCase_, lowerCamelCase_, 'tf' )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = FeatureExtractionPipeline(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = infer_shapes(lowerCamelCase_, lowerCamelCase_ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase_ ), len(lowerCamelCase_ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3], lowerCamelCase_ )
self.assertSequenceEqual(variable_names[3:], lowerCamelCase_ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name], {0: 'batch', 1: 'sequence'} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['output_0'], {0: 'batch', 1: 'sequence'} )
self.assertDictEqual(shapes['output_1'], {0: 'batch'} )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = ['input_ids', 'attention_mask', 'token_type_ids']
lowerCamelCase__ : Optional[int] = {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
lowerCamelCase__ , lowerCamelCase__ : str = ensure_valid_input(FuncContiguousArgs(), lowerCamelCase_, lowerCamelCase_ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase_ ), 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase_ ), set(lowerCamelCase_ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase_, (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowerCamelCase__ , lowerCamelCase__ : Any = ensure_valid_input(FuncNonContiguousArgs(), lowerCamelCase_, lowerCamelCase_ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase_ ), 1 )
self.assertEqual(len(lowerCamelCase_ ), 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0], tokens['input_ids'] )
self.assertEqual(ordered_input_names[0], 'input_ids' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = generate_identified_filename(Path('/home/something/my_fake_model.onnx' ), '-test' )
self.assertEqual('/home/something/my_fake_model-test.onnx', generated.as_posix() )
| 696 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_lowerCamelCase ) * abs(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 696 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : int = KandinskyVaaControlnetImgaImgPipeline
lowerCamelCase__ : Optional[int] = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
lowerCamelCase__ : Dict = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
lowerCamelCase__ : str = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowerCamelCase__ : Any = False
@property
def a__ (self ):
'''simple docstring'''
return 3_2
@property
def a__ (self ):
'''simple docstring'''
return 3_2
@property
def a__ (self ):
'''simple docstring'''
return self.time_input_dim
@property
def a__ (self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def a__ (self ):
'''simple docstring'''
return 1_0_0
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Optional[int] = {
'in_channels': 8,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image_hint',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
lowerCamelCase__ : int = UNetaDConditionModel(**lowerCamelCase_ )
return model
@property
def a__ (self ):
'''simple docstring'''
return {
"block_out_channels": [3_2, 3_2, 6_4, 6_4],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.dummy_unet
lowerCamelCase__ : List[Any] = self.dummy_movq
lowerCamelCase__ : Tuple = {
'num_train_timesteps': 1_0_0_0,
'beta_schedule': 'linear',
'beta_start': 0.00_085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
lowerCamelCase__ : Optional[Any] = DDIMScheduler(**lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def a__ (self, lowerCamelCase_, lowerCamelCase_=0 ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
lowerCamelCase__ : int = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1 ) ).to(
lowerCamelCase_ )
# create init_image
lowerCamelCase__ : Any = floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
lowerCamelCase__ : Dict = image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase__ : Optional[Any] = Image.fromarray(np.uinta(lowerCamelCase_ ) ).convert('RGB' ).resize((2_5_6, 2_5_6) )
# create hint
lowerCamelCase__ : Dict = floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
if str(lowerCamelCase_ ).startswith('mps' ):
lowerCamelCase__ : int = torch.manual_seed(lowerCamelCase_ )
else:
lowerCamelCase__ : Any = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = {
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'hint': hint,
'generator': generator,
'height': 6_4,
'width': 6_4,
'num_inference_steps': 1_0,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = 'cpu'
lowerCamelCase__ : List[Any] = self.get_dummy_components()
lowerCamelCase__ : List[Any] = self.pipeline_class(**lowerCamelCase_ )
lowerCamelCase__ : Dict = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : Any = pipe(**self.get_dummy_inputs(lowerCamelCase_ ) )
lowerCamelCase__ : List[Any] = output.images
lowerCamelCase__ : str = pipe(
**self.get_dummy_inputs(lowerCamelCase_ ), return_dict=lowerCamelCase_, )[0]
lowerCamelCase__ : int = image[0, -3:, -3:, -1]
lowerCamelCase__ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowerCamelCase__ : List[str] = np.array(
[0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy' )
lowerCamelCase__ : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
lowerCamelCase__ : Any = init_image.resize((5_1_2, 5_1_2) )
lowerCamelCase__ : List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/hint_image_cat.png' )
lowerCamelCase__ : Any = torch.from_numpy(np.array(lowerCamelCase_ ) ).float() / 255.0
lowerCamelCase__ : Optional[int] = hint.permute(2, 0, 1 ).unsqueeze(0 )
lowerCamelCase__ : Union[str, Any] = 'A robot, 4k photo'
lowerCamelCase__ : Any = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior', torch_dtype=torch.floataa )
pipe_prior.to(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-controlnet-depth', torch_dtype=torch.floataa )
lowerCamelCase__ : int = pipeline.to(lowerCamelCase_ )
pipeline.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : str = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = pipe_prior(
lowerCamelCase_, image=lowerCamelCase_, strength=0.85, generator=lowerCamelCase_, negative_prompt='', ).to_tuple()
lowerCamelCase__ : Union[str, Any] = pipeline(
image=lowerCamelCase_, image_embeds=lowerCamelCase_, negative_image_embeds=lowerCamelCase_, hint=lowerCamelCase_, generator=lowerCamelCase_, num_inference_steps=1_0_0, height=5_1_2, width=5_1_2, strength=0.5, output_type='np', )
lowerCamelCase__ : Dict = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase = 3 , _lowerCamelCase = 7 , _lowerCamelCase = 100_0000 ):
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Optional[Any] = 1
for current_denominator in range(1 , limit + 1 ):
lowerCamelCase__ : Optional[int] = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
lowerCamelCase__ : List[str] = current_numerator
lowerCamelCase__ : Optional[int] = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1_00_00_00))
| 696 |
"""simple docstring"""
A_ : List[str] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dataclasses": "dataclasses",
"datasets": "datasets!=2.5.0",
"decord": "decord==0.6.0",
"deepspeed": "deepspeed>=0.9.3",
"diffusers": "diffusers",
"dill": "dill<0.3.5",
"evaluate": "evaluate>=0.2.0",
"fairscale": "fairscale>0.3",
"faiss-cpu": "faiss-cpu",
"fastapi": "fastapi",
"filelock": "filelock",
"flax": "flax>=0.4.1,<=0.7.0",
"ftfy": "ftfy",
"fugashi": "fugashi>=1.0",
"GitPython": "GitPython<3.1.19",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
"importlib_metadata": "importlib_metadata",
"ipadic": "ipadic>=1.0.0,<2.0",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
"jaxlib": "jaxlib>=0.1.65,<=0.4.13",
"jieba": "jieba",
"kenlm": "kenlm",
"keras-nlp": "keras-nlp>=0.3.1",
"librosa": "librosa",
"nltk": "nltk",
"natten": "natten>=0.14.6",
"numpy": "numpy>=1.17",
"onnxconverter-common": "onnxconverter-common",
"onnxruntime-tools": "onnxruntime-tools>=1.4.2",
"onnxruntime": "onnxruntime>=1.4.0",
"opencv-python": "opencv-python",
"optuna": "optuna",
"optax": "optax>=0.0.8,<=0.1.4",
"packaging": "packaging>=20.0",
"parameterized": "parameterized",
"phonemizer": "phonemizer",
"protobuf": "protobuf",
"psutil": "psutil",
"pyyaml": "pyyaml>=5.1",
"pydantic": "pydantic<2",
"pytest": "pytest>=7.2.0",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"python": "python>=3.8.0",
"ray[tune]": "ray[tune]",
"regex": "regex!=2019.12.17",
"requests": "requests",
"rhoknp": "rhoknp>=1.1.0,<1.3.1",
"rjieba": "rjieba",
"rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
"ruff": "ruff>=0.0.241,<=0.0.259",
"sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
"sacremoses": "sacremoses",
"safetensors": "safetensors>=0.3.1",
"sagemaker": "sagemaker>=2.31.0",
"scikit-learn": "scikit-learn",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"sigopt": "sigopt",
"starlette": "starlette",
"sudachipy": "sudachipy>=0.6.6",
"sudachidict_core": "sudachidict_core>=20220729",
"tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
"tensorflow": "tensorflow>=2.6,<2.14",
"tensorflow-text": "tensorflow-text<2.14",
"tf2onnx": "tf2onnx",
"timeout-decorator": "timeout-decorator",
"timm": "timm",
"tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
"torch": "torch>=1.9,!=1.12.0",
"torchaudio": "torchaudio",
"torchvision": "torchvision",
"pyctcdecode": "pyctcdecode>=0.4.0",
"tqdm": "tqdm>=4.27",
"unidic": "unidic>=1.0.2",
"unidic_lite": "unidic_lite>=1.0.7",
"urllib3": "urllib3<2.0.0",
"uvicorn": "uvicorn",
}
| 696 | 1 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def lowerCamelCase_ ( _lowerCamelCase ):
if num <= 0:
raise ValueError('math domain error' )
return quad(_lowerCamelCase , 0 , _lowerCamelCase , args=(_lowerCamelCase) )[0]
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
return math.pow(_lowerCamelCase , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 696 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
A_ : Optional[int] = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_ = 1_4 ):
'''simple docstring'''
if group not in primes:
raise ValueError('Unsupported Group' )
lowerCamelCase__ : int = primes[group]['prime']
lowerCamelCase__ : Optional[int] = primes[group]['generator']
lowerCamelCase__ : Any = int(hexlify(urandom(3_2 ) ), base=1_6 )
def a__ (self ):
'''simple docstring'''
return hex(self.__private_key )[2:]
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = pow(self.generator, self.__private_key, self.prime )
return hex(lowerCamelCase_ )[2:]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
return (
2 <= key <= self.prime - 2
and pow(lowerCamelCase_, (self.prime - 1) // 2, self.prime ) == 1
)
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = int(lowerCamelCase_, base=1_6 )
if not self.is_valid_public_key(lowerCamelCase_ ):
raise ValueError('Invalid public key' )
lowerCamelCase__ : Tuple = pow(lowerCamelCase_, self.__private_key, self.prime )
return shaaaa(str(lowerCamelCase_ ).encode() ).hexdigest()
@staticmethod
def a__ (lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return (
2 <= remote_public_key_str <= prime - 2
and pow(lowerCamelCase_, (prime - 1) // 2, lowerCamelCase_ ) == 1
)
@staticmethod
def a__ (lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ = 1_4 ):
'''simple docstring'''
lowerCamelCase__ : Dict = int(lowerCamelCase_, base=1_6 )
lowerCamelCase__ : List[Any] = int(lowerCamelCase_, base=1_6 )
lowerCamelCase__ : List[str] = primes[group]['prime']
if not DiffieHellman.is_valid_public_key_static(lowerCamelCase_, lowerCamelCase_ ):
raise ValueError('Invalid public key' )
lowerCamelCase__ : Dict = pow(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
return shaaaa(str(lowerCamelCase_ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 | 1 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
A_ : Tuple = [
{"dataset": "wikipedia", "config_name": "20220301.de"},
{"dataset": "wikipedia", "config_name": "20220301.en"},
{"dataset": "wikipedia", "config_name": "20220301.fr"},
{"dataset": "wikipedia", "config_name": "20220301.frr"},
{"dataset": "wikipedia", "config_name": "20220301.it"},
{"dataset": "wikipedia", "config_name": "20220301.simple"},
{"dataset": "snli", "config_name": "plain_text"},
{"dataset": "eli5", "config_name": "LFQA_reddit"},
{"dataset": "wiki40b", "config_name": "en"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
{"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
{"dataset": "natural_questions", "config_name": "default"},
]
def lowerCamelCase_ ( _lowerCamelCase=True ):
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=snake_case_ ) )
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Optional[Any] = None
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
with TemporaryDirectory() as tmp_dir:
lowerCamelCase__ : Optional[int] = dataset_module_factory(lowerCamelCase_, cache_dir=lowerCamelCase_ )
lowerCamelCase__ : Any = import_main_class(dataset_module.module_path, dataset=lowerCamelCase_ )
lowerCamelCase__ : DatasetBuilder = builder_cls(
cache_dir=lowerCamelCase_, config_name=lowerCamelCase_, hash=dataset_module.hash, )
lowerCamelCase__ : str = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=lowerCamelCase_ ).replace(os.sep, '/' ),
config.DATASET_INFO_FILENAME,
] )
lowerCamelCase__ : Optional[Any] = cached_path(lowerCamelCase_, cache_dir=lowerCamelCase_ )
self.assertTrue(os.path.exists(lowerCamelCase_ ) )
@pytest.mark.integration
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[Any] = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
lowerCamelCase__ : List[Any] = dataset_module_factory('wikipedia' , cache_dir=_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = import_main_class(dataset_module.module_path )
lowerCamelCase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCamelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
lowerCamelCase__ : Optional[int] = None
builder_instance.download_and_prepare()
lowerCamelCase__ : str = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = dataset_module_factory('wikipedia' , cache_dir=_lowerCamelCase )
lowerCamelCase__ : List[str] = import_main_class(dataset_module.module_path , dataset=_lowerCamelCase )
lowerCamelCase__ : DatasetBuilder = builder_cls(
cache_dir=_lowerCamelCase , config_name='20220301.frr' , hash=dataset_module.hash , )
lowerCamelCase__ : List[str] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_lowerCamelCase , _lowerCamelCase )
assert "train" in ds
assert isinstance(ds['train'] , _lowerCamelCase )
assert next(iter(ds['train'] ) )
| 696 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_lowerCamelCase ) * abs(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 696 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Union[List[PIL.Image.Image], np.ndarray]
lowerCamelCase__ : Optional[List[bool]]
lowerCamelCase__ : Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
| 696 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
A_ : int = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 1_28,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
@is_staging_test
class a_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def a__ (cls ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def a__ (cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='test-dynamic-config' )
except HTTPError:
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('test-config', use_auth_token=self._token )
lowerCamelCase__ : Optional[int] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase_, repo_id='test-config', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : List[str] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org', use_auth_token=self._token )
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase_, repo_id='valid_org/test-config-org', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : str = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
CustomConfig.register_for_auto_class()
lowerCamelCase__ : Optional[int] = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config', use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map, {'AutoConfig': 'custom_configuration.CustomConfig'} )
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config''', trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__, 'CustomConfig' )
self.assertEqual(new_config.attribute, 4_2 )
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
lowerCamelCase__ : Tuple = c.n_embd + 1 # int
lowerCamelCase__ : Union[str, Any] = c.resid_pdrop + 1.0 # float
lowerCamelCase__ : List[Any] = not c.scale_attn_weights # bool
lowerCamelCase__ : List[Any] = c.summary_type + 'foo' # str
c.update_from_string(
f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(lowerCamelCase_, c.n_embd, 'mismatch for key: n_embd' )
self.assertEqual(lowerCamelCase_, c.resid_pdrop, 'mismatch for key: resid_pdrop' )
self.assertEqual(lowerCamelCase_, c.scale_attn_weights, 'mismatch for key: scale_attn_weights' )
self.assertEqual(lowerCamelCase_, c.summary_type, 'mismatch for key: summary_type' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = PretrainedConfig()
lowerCamelCase__ : Optional[Any] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowerCamelCase_, ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
lowerCamelCase__ : Any = [key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase_, lowerCamelCase_ )]
if len(lowerCamelCase_ ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
f''' {', '.join(lowerCamelCase_ )}.''' )
def a__ (self ):
'''simple docstring'''
with self.assertRaises(lowerCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
lowerCamelCase__ : int = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder', subfolder='bert' )
self.assertIsNotNone(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = mock.Mock()
lowerCamelCase__ : List[str] = 5_0_0
lowerCamelCase__ : Any = {}
lowerCamelCase__ : int = HTTPError
lowerCamelCase__ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
lowerCamelCase__ : Any = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request', return_value=lowerCamelCase_ ) as mock_head:
lowerCamelCase__ : List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = AutoConfig.from_pretrained('bert-base-cased' )
lowerCamelCase__ : str = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = 2
json.dump(configuration.to_dict(), open(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
lowerCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
lowerCamelCase__ : str = ['config.42.0.0.json']
lowerCamelCase__ : Union[str, Any] = 7_6_8
configuration.save_pretrained(lowerCamelCase_ )
shutil.move(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), os.path.join(lowerCamelCase_, 'config.42.0.0.json' ) )
lowerCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 7_6_8 )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
lowerCamelCase__ : Optional[int] = 'v4.0.0'
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
lowerCamelCase_, return_unused_kwargs=lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowerCamelCase_, {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
lowerCamelCase__ : Dict = 'v3.0.0'
lowerCamelCase__ : List[str] = old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(old_configuration.hidden_size, 7_6_8 )
| 696 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : str = len(_lowerCamelCase )
while cur > 1:
# Find the maximum number in arr
lowerCamelCase__ : str = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowerCamelCase__ : List[str] = arr[mi::-1] + arr[mi + 1 : len(_lowerCamelCase )]
# Reverse whole list
lowerCamelCase__ : Any = arr[cur - 1 :: -1] + arr[cur : len(_lowerCamelCase )]
cur -= 1
return arr
if __name__ == "__main__":
A_ : Optional[Any] = input("Enter numbers separated by a comma:\n").strip()
A_ : Tuple = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 696 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
super().__init__()
lowerCamelCase__ : Dict = value_function
lowerCamelCase__ : int = unet
lowerCamelCase__ : Union[str, Any] = scheduler
lowerCamelCase__ : int = env
lowerCamelCase__ : List[Any] = env.get_dataset()
lowerCamelCase__ : Dict = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
lowerCamelCase__ : Optional[int] = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Tuple = self.data[key].std()
except: # noqa: E722
pass
lowerCamelCase__ : Optional[Any] = env.observation_space.shape[0]
lowerCamelCase__ : List[str] = env.action_space.shape[0]
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
if type(lowerCamelCase_ ) is dict:
return {k: self.to_torch(lowerCamelCase_ ) for k, v in x_in.items()}
elif torch.is_tensor(lowerCamelCase_ ):
return x_in.to(self.unet.device )
return torch.tensor(lowerCamelCase_, device=self.unet.device )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
for key, val in cond.items():
lowerCamelCase__ : Optional[Any] = val.clone()
return x_in
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = x.shape[0]
lowerCamelCase__ : Tuple = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowerCamelCase__ : Dict = torch.full((batch_size,), lowerCamelCase_, device=self.unet.device, dtype=torch.long )
for _ in range(lowerCamelCase_ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowerCamelCase__ : str = self.value_function(x.permute(0, 2, 1 ), lowerCamelCase_ ).sample
lowerCamelCase__ : Union[str, Any] = torch.autograd.grad([y.sum()], [x] )[0]
lowerCamelCase__ : Optional[int] = self.scheduler._get_variance(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = torch.exp(0.5 * posterior_variance )
lowerCamelCase__ : Tuple = model_std * grad
lowerCamelCase__ : str = 0
lowerCamelCase__ : Dict = x.detach()
lowerCamelCase__ : Dict = x + scale * grad
lowerCamelCase__ : Optional[int] = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : Tuple = self.unet(x.permute(0, 2, 1 ), lowerCamelCase_ ).sample.permute(0, 2, 1 )
# TODO: verify deprecation of this kwarg
lowerCamelCase__ : Optional[Any] = self.scheduler.step(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, predict_epsilon=lowerCamelCase_ )['prev_sample']
# apply conditions to the trajectory (set the initial state)
lowerCamelCase__ : Any = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : List[str] = self.to_torch(lowerCamelCase_ )
return x, y
def __call__(self, lowerCamelCase_, lowerCamelCase_=6_4, lowerCamelCase_=3_2, lowerCamelCase_=2, lowerCamelCase_=0.1 ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.normalize(lowerCamelCase_, 'observations' )
lowerCamelCase__ : List[str] = obs[None].repeat(lowerCamelCase_, axis=0 )
lowerCamelCase__ : str = {0: self.to_torch(lowerCamelCase_ )}
lowerCamelCase__ : Optional[Any] = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowerCamelCase__ : List[Any] = randn_tensor(lowerCamelCase_, device=self.unet.device )
lowerCamelCase__ : int = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : List[str] = self.to_torch(lowerCamelCase_ )
# run the diffusion process
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.run_diffusion(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
# sort output trajectories by value
lowerCamelCase__ : Union[str, Any] = y.argsort(0, descending=lowerCamelCase_ ).squeeze()
lowerCamelCase__ : List[str] = x[sorted_idx]
lowerCamelCase__ : Optional[Any] = sorted_values[:, :, : self.action_dim]
lowerCamelCase__ : Union[str, Any] = actions.detach().cpu().numpy()
lowerCamelCase__ : Union[str, Any] = self.de_normalize(lowerCamelCase_, key='actions' )
# select the action with the highest value
if y is not None:
lowerCamelCase__ : str = 0
else:
# if we didn't run value guiding, select a random action
lowerCamelCase__ : Optional[Any] = np.random.randint(0, lowerCamelCase_ )
lowerCamelCase__ : Tuple = denorm_actions[selected_index, 0]
return denorm_actions
| 696 | 1 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[int] = prime_factors(_lowerCamelCase )
if is_square_free(_lowerCamelCase ):
return -1 if len(_lowerCamelCase ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ , lowerCamelCase__ : List[str] = analyze_text(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = list(' ' + ascii_lowercase )
# what is our total sum of probabilities.
lowerCamelCase__ : List[Any] = sum(single_char_strings.values() )
# one length string
lowerCamelCase__ : str = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
lowerCamelCase__ : Tuple = single_char_strings[ch]
lowerCamelCase__ : Union[str, Any] = my_str / all_sum
my_fir_sum += prob * math.loga(_lowerCamelCase ) # entropy formula.
# print entropy
print(f'''{round(-1 * my_fir_sum ):.1f}''' )
# two len string
lowerCamelCase__ : Dict = sum(two_char_strings.values() )
lowerCamelCase__ : str = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
lowerCamelCase__ : int = cha + cha
if sequence in two_char_strings:
lowerCamelCase__ : int = two_char_strings[sequence]
lowerCamelCase__ : Tuple = int(_lowerCamelCase ) / all_sum
my_sec_sum += prob * math.loga(_lowerCamelCase )
# print second entropy
print(f'''{round(-1 * my_sec_sum ):.1f}''' )
# print the difference between them
print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}''' )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[str] = Counter() # type: ignore
lowerCamelCase__ : List[Any] = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(_lowerCamelCase ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def lowerCamelCase_ ( ):
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 696 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Dict = len(_lowerCamelCase )
lowerCamelCase__ : Dict = len(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
lowerCamelCase__ : str = True
for i in range(_lowerCamelCase ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
lowerCamelCase__ : Optional[int] = True
if a[i].islower():
lowerCamelCase__ : List[str] = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 |
"""simple docstring"""
import os
def lowerCamelCase_ ( ):
with open(os.path.dirname(_lowerCamelCase ) + '/p022_names.txt' ) as file:
lowerCamelCase__ : Union[str, Any] = str(file.readlines()[0] )
lowerCamelCase__ : int = names.replace('"' , '' ).split(',' )
names.sort()
lowerCamelCase__ : Tuple = 0
lowerCamelCase__ : str = 0
for i, name in enumerate(_lowerCamelCase ):
for letter in name:
name_score += ord(_lowerCamelCase ) - 64
total_score += (i + 1) * name_score
lowerCamelCase__ : Dict = 0
return total_score
if __name__ == "__main__":
print(solution())
| 696 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
A_ : int = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
A_ : Optional[int] = BASE_URL + "/user"
# https://github.com/settings/tokens
A_ : int = os.environ.get("USER_TOKEN", "")
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[str] = {
'Authorization': f'''token {auth_token}''',
'Accept': 'application/vnd.github.v3+json',
}
return requests.get(_lowerCamelCase , headers=_lowerCamelCase ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f"{key}: {value}")
else:
raise ValueError("'USER_TOKEN' field cannot be empty.")
| 696 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : int = 'Speech2TextFeatureExtractor'
lowerCamelCase__ : Dict = 'Speech2TextTokenizer'
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
super().__init__(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : List[str] = self.feature_extractor
lowerCamelCase__ : List[Any] = False
def __call__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*lowerCamelCase_, **lowerCamelCase_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
lowerCamelCase__ : Optional[int] = kwargs.pop('raw_speech' )
else:
lowerCamelCase__ : int = kwargs.pop('audio', lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = kwargs.pop('sampling_rate', lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = kwargs.pop('text', lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
lowerCamelCase__ : List[str] = args[0]
lowerCamelCase__ : Any = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
lowerCamelCase__ : Union[str, Any] = self.feature_extractor(lowerCamelCase_, *lowerCamelCase_, sampling_rate=lowerCamelCase_, **lowerCamelCase_ )
if text is not None:
lowerCamelCase__ : List[Any] = self.tokenizer(lowerCamelCase_, **lowerCamelCase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCamelCase__ : Tuple = encodings['input_ids']
return inputs
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase_, **lowerCamelCase_ )
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase_, **lowerCamelCase_ )
@contextmanager
def a__ (self ):
'''simple docstring'''
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
lowerCamelCase__ : int = True
lowerCamelCase__ : List[Any] = self.tokenizer
yield
lowerCamelCase__ : Optional[int] = self.feature_extractor
lowerCamelCase__ : List[Any] = False
| 696 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def lowerCamelCase_ ( ):
lowerCamelCase__ : Union[str, Any] = ArgumentParser('Accelerate CLI tool' , usage='accelerate <command> [<args>]' , allow_abbrev=_lowerCamelCase )
lowerCamelCase__ : List[str] = parser.add_subparsers(help='accelerate command helpers' )
# Register commands
get_config_parser(subparsers=_lowerCamelCase )
env_command_parser(subparsers=_lowerCamelCase )
launch_command_parser(subparsers=_lowerCamelCase )
tpu_command_parser(subparsers=_lowerCamelCase )
test_command_parser(subparsers=_lowerCamelCase )
# Let's go
lowerCamelCase__ : int = parser.parse_args()
if not hasattr(_lowerCamelCase , 'func' ):
parser.print_help()
exit(1 )
# Run
args.func(_lowerCamelCase )
if __name__ == "__main__":
main()
| 696 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=9_9, lowerCamelCase_=6_4, lowerCamelCase_=3_2, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = parent
lowerCamelCase__ : Union[str, Any] = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : List[str] = is_training
lowerCamelCase__ : Optional[Any] = use_input_mask
lowerCamelCase__ : List[Any] = use_token_type_ids
lowerCamelCase__ : List[Any] = use_labels
lowerCamelCase__ : Optional[Any] = vocab_size
lowerCamelCase__ : str = hidden_size
lowerCamelCase__ : Optional[int] = embedding_size
lowerCamelCase__ : List[str] = num_hidden_layers
lowerCamelCase__ : Any = num_attention_heads
lowerCamelCase__ : Any = intermediate_size
lowerCamelCase__ : Union[str, Any] = hidden_act
lowerCamelCase__ : str = hidden_dropout_prob
lowerCamelCase__ : Tuple = attention_probs_dropout_prob
lowerCamelCase__ : Any = max_position_embeddings
lowerCamelCase__ : Any = type_vocab_size
lowerCamelCase__ : List[Any] = type_sequence_label_size
lowerCamelCase__ : Dict = initializer_range
lowerCamelCase__ : Optional[Any] = num_labels
lowerCamelCase__ : Dict = num_choices
lowerCamelCase__ : Tuple = scope
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : List[str] = None
if self.use_input_mask:
lowerCamelCase__ : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Any = None
if self.use_token_type_ids:
lowerCamelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : Any = None
lowerCamelCase__ : Union[str, Any] = None
if self.use_labels:
lowerCamelCase__ : int = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : int = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : str = ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ):
'''simple docstring'''
return MobileBertConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, embedding_size=self.embedding_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=lowerCamelCase_, initializer_range=self.initializer_range, )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = MobileBertModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_, token_type_ids=lowerCamelCase_ )
lowerCamelCase__ : Tuple = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = MobileBertForMaskedLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = MobileBertForNextSentencePrediction(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : str = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = MobileBertForPreTraining(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, next_sentence_label=lowerCamelCase_, )
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = MobileBertForQuestionAnswering(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, start_positions=lowerCamelCase_, end_positions=lowerCamelCase_, )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : int = MobileBertForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.num_labels
lowerCamelCase__ : Optional[int] = MobileBertForTokenClassification(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : int = self.num_choices
lowerCamelCase__ : Dict = MobileBertForMultipleChoice(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = input_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : int = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : List[str] = config_and_inputs
lowerCamelCase__ : Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Dict = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : Tuple = (
{
'feature-extraction': MobileBertModel,
'fill-mask': MobileBertForMaskedLM,
'question-answering': MobileBertForQuestionAnswering,
'text-classification': MobileBertForSequenceClassification,
'token-classification': MobileBertForTokenClassification,
'zero-shot': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : int = True
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=False ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = super()._prepare_for_class(lowerCamelCase_, lowerCamelCase_, return_labels=lowerCamelCase_ )
if return_labels:
if model_class in get_values(lowerCamelCase_ ):
lowerCamelCase__ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=lowerCamelCase_ )
return inputs_dict
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = MobileBertModelTester(self )
lowerCamelCase__ : List[str] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=3_7 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
return torch.tensor(
_lowerCamelCase , dtype=torch.long , device=_lowerCamelCase , )
A_ : Tuple = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = MobileBertModel.from_pretrained('google/mobilebert-uncased' ).to(lowerCamelCase_ )
lowerCamelCase__ : Tuple = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] )
with torch.no_grad():
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_ )[0]
lowerCamelCase__ : Optional[int] = torch.Size((1, 9, 5_1_2) )
self.assertEqual(output.shape, lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = torch.tensor(
[
[
[-2.4_736_526e07, 8.2_691_656e04, 1.6_521_838e05],
[-5.7_541_704e-01, 3.9_056_022e00, 4.4_011_507e00],
[2.6_047_359e00, 1.5_677_652e00, -1.7_324_188e-01],
]
], device=lowerCamelCase_, )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
lowerCamelCase__ : Optional[int] = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
lowerCamelCase__ : Any = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 696 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a_ ( snake_case_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a__ (lowerCamelCase_ ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def a__ (self ):
'''simple docstring'''
raise NotImplementedError()
| 696 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
A_ : str = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=1 ):
'''simple docstring'''
lowerCamelCase__ : Any = tokenizer
lowerCamelCase__ : Optional[Any] = dataset
lowerCamelCase__ : int = len(lowerCamelCase_ ) if n_tasks is None else n_tasks
lowerCamelCase__ : Any = n_copies
def __iter__(self ):
'''simple docstring'''
lowerCamelCase__ : Dict = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
lowerCamelCase__ : Optional[int] = self.tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = start_length
lowerCamelCase__ : List[str] = eof_strings
lowerCamelCase__ : List[str] = tokenizer
def __call__(self, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
lowerCamelCase__ : Optional[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = re.split('(%s)' % '|'.join(_lowerCamelCase ) , _lowerCamelCase )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=20 , **_lowerCamelCase ):
lowerCamelCase__ : List[str] = defaultdict(_lowerCamelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_lowerCamelCase ) ):
with torch.no_grad():
lowerCamelCase__ : str = batch['ids'].shape[-1]
lowerCamelCase__ : int = accelerator.unwrap_model(_lowerCamelCase ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=_lowerCamelCase , **_lowerCamelCase )
# each task is generated batch_size times
lowerCamelCase__ : Optional[Any] = batch['task_id'].repeat(_lowerCamelCase )
lowerCamelCase__ : List[Any] = accelerator.pad_across_processes(
_lowerCamelCase , dim=1 , pad_index=tokenizer.pad_token_id )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
lowerCamelCase__ : List[Any] = generated_tokens.cpu().numpy()
lowerCamelCase__ : Union[str, Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_lowerCamelCase , _lowerCamelCase ):
gen_token_dict[task].append(_lowerCamelCase )
lowerCamelCase__ : str = [[] for _ in range(_lowerCamelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
lowerCamelCase__ : Optional[Any] = tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
code_gens[task].append(remove_last_block(_lowerCamelCase ) )
return code_gens
def lowerCamelCase_ ( ):
# Setup configuration
lowerCamelCase__ : int = HfArgumentParser(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
lowerCamelCase__ : List[str] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
lowerCamelCase__ : Tuple = 'false'
if args.num_workers is None:
lowerCamelCase__ : List[Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
lowerCamelCase__ : List[Any] = Accelerator()
set_seed(args.seed , device_specific=_lowerCamelCase )
# Load model and tokenizer
lowerCamelCase__ : Any = AutoTokenizer.from_pretrained(args.model_ckpt )
lowerCamelCase__ : Optional[int] = tokenizer.eos_token
lowerCamelCase__ : Any = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
lowerCamelCase__ : Optional[Any] = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , _lowerCamelCase , _lowerCamelCase )] ),
}
# Load evaluation dataset and metric
lowerCamelCase__ : Any = load_dataset('openai_humaneval' )
lowerCamelCase__ : Optional[int] = load_metric('code_eval' )
lowerCamelCase__ : List[Any] = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
lowerCamelCase__ : Optional[int] = args.n_samples // args.batch_size
lowerCamelCase__ : Tuple = TokenizedDataset(_lowerCamelCase , human_eval['test'] , n_copies=_lowerCamelCase , n_tasks=_lowerCamelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
lowerCamelCase__ : Union[str, Any] = DataLoader(_lowerCamelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
lowerCamelCase__ : List[Any] = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
lowerCamelCase__ , lowerCamelCase__ : str = accelerator.prepare(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Any = complete_code(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , n_tasks=_lowerCamelCase , batch_size=args.batch_size , **_lowerCamelCase , )
if accelerator.is_main_process:
lowerCamelCase__ : List[str] = []
for task in tqdm(range(_lowerCamelCase ) ):
lowerCamelCase__ : int = human_eval['test'][task]['test']
lowerCamelCase__ : Union[str, Any] = f'''check({human_eval['test'][task]['entry_point']})'''
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
lowerCamelCase__ , lowerCamelCase__ : Any = code_eval_metric.compute(
references=_lowerCamelCase , predictions=_lowerCamelCase , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 696 | 1 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
A_ : Union[str, Any] = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if got_ver is None or want_ver is None:
raise ValueError(
f'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
f''' reinstalling {pkg}.''' )
if not ops[op](version.parse(_lowerCamelCase ) , version.parse(_lowerCamelCase ) ):
raise ImportError(
f'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''' )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase = None ):
lowerCamelCase__ : Optional[Any] = f'''\n{hint}''' if hint is not None else ''
# non-versioned check
if re.match(r'^[\w_\-\d]+$' , _lowerCamelCase ):
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = requirement, None, None
else:
lowerCamelCase__ : List[str] = re.findall(r'^([^!=<>\s]+)([\s!=<>]{1,2}.+)' , _lowerCamelCase )
if not match:
raise ValueError(
'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
f''' got {requirement}''' )
lowerCamelCase__ , lowerCamelCase__ : List[str] = match[0]
lowerCamelCase__ : List[Any] = want_full.split(',' ) # there could be multiple requirements
lowerCamelCase__ : Any = {}
for w in want_range:
lowerCamelCase__ : Optional[Any] = re.findall(r'^([\s!=<>]{1,2})(.+)' , _lowerCamelCase )
if not match:
raise ValueError(
'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
f''' but got {requirement}''' )
lowerCamelCase__ , lowerCamelCase__ : List[str] = match[0]
lowerCamelCase__ : List[str] = want_ver
if op not in ops:
raise ValueError(f'''{requirement}: need one of {list(ops.keys() )}, but got {op}''' )
# special case
if pkg == "python":
lowerCamelCase__ : str = '.'.join([str(_lowerCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return
# check if any version is installed
try:
lowerCamelCase__ : int = importlib.metadata.version(_lowerCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
f'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''' )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = 'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
return require_version(_lowerCamelCase , _lowerCamelCase )
| 696 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : str = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
requires_backends(self, ['speech'] )
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
requires_backends(self, ['speech'] )
| 696 | 1 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=1e-12 ):
lowerCamelCase__ : List[Any] = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(_lowerCamelCase , axis=1 ) , a_min=_lowerCamelCase ) ).T
lowerCamelCase__ : Optional[Any] = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(_lowerCamelCase , axis=1 ) , a_min=_lowerCamelCase ) ).T
return jnp.matmul(_lowerCamelCase , norm_emb_a.T )
class a_ ( nn.Module ):
'''simple docstring'''
lowerCamelCase__ : CLIPConfig
lowerCamelCase__ : jnp.dtype = jnp.floataa
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = FlaxCLIPVisionModule(self.config.vision_config )
lowerCamelCase__ : str = nn.Dense(self.config.projection_dim, use_bias=lowerCamelCase_, dtype=self.dtype )
lowerCamelCase__ : Optional[int] = self.param('concept_embeds', jax.nn.initializers.ones, (1_7, self.config.projection_dim) )
lowerCamelCase__ : Optional[int] = self.param(
'special_care_embeds', jax.nn.initializers.ones, (3, self.config.projection_dim) )
lowerCamelCase__ : List[Any] = self.param('concept_embeds_weights', jax.nn.initializers.ones, (1_7,) )
lowerCamelCase__ : Any = self.param('special_care_embeds_weights', jax.nn.initializers.ones, (3,) )
def __call__(self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = self.vision_model(lowerCamelCase_ )[1]
lowerCamelCase__ : Optional[Any] = self.visual_projection(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = jax_cosine_distance(lowerCamelCase_, self.special_care_embeds )
lowerCamelCase__ : List[Any] = jax_cosine_distance(lowerCamelCase_, self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
lowerCamelCase__ : Optional[int] = 0.0
lowerCamelCase__ : Optional[Any] = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
lowerCamelCase__ : Any = jnp.round(lowerCamelCase_, 3 )
lowerCamelCase__ : Union[str, Any] = jnp.any(special_scores > 0, axis=1, keepdims=lowerCamelCase_ )
# Use a lower threshold if an image has any special care concept
lowerCamelCase__ : str = is_special_care * 0.01
lowerCamelCase__ : Tuple = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
lowerCamelCase__ : List[str] = jnp.round(lowerCamelCase_, 3 )
lowerCamelCase__ : int = jnp.any(concept_scores > 0, axis=1 )
return has_nsfw_concepts
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = CLIPConfig
lowerCamelCase__ : Any = 'clip_input'
lowerCamelCase__ : Optional[int] = FlaxStableDiffusionSafetyCheckerModule
def __init__(self, lowerCamelCase_, lowerCamelCase_ = None, lowerCamelCase_ = 0, lowerCamelCase_ = jnp.floataa, lowerCamelCase_ = True, **lowerCamelCase_, ):
'''simple docstring'''
if input_shape is None:
lowerCamelCase__ : str = (1, 2_2_4, 2_2_4, 3)
lowerCamelCase__ : Dict = self.module_class(config=lowerCamelCase_, dtype=lowerCamelCase_, **lowerCamelCase_ )
super().__init__(lowerCamelCase_, lowerCamelCase_, input_shape=lowerCamelCase_, seed=lowerCamelCase_, dtype=lowerCamelCase_, _do_init=_do_init )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ = None ):
'''simple docstring'''
lowerCamelCase__ : str = jax.random.normal(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ , lowerCamelCase__ : Any = jax.random.split(lowerCamelCase_ )
lowerCamelCase__ : int = {'params': params_rng, 'dropout': dropout_rng}
lowerCamelCase__ : str = self.module.init(lowerCamelCase_, lowerCamelCase_ )['params']
return random_params
def __call__(self, lowerCamelCase_, lowerCamelCase_ = None, ):
'''simple docstring'''
lowerCamelCase__ : str = jnp.transpose(lowerCamelCase_, (0, 2, 3, 1) )
return self.module.apply(
{'params': params or self.params}, jnp.array(lowerCamelCase_, dtype=jnp.floataa ), rngs={}, )
| 696 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = 0
while number > 0:
lowerCamelCase__ : List[str] = number % 10
sum_of_digits += last_digit
lowerCamelCase__ : str = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def lowerCamelCase_ ( _lowerCamelCase = 100 ):
lowerCamelCase__ : Union[str, Any] = factorial(_lowerCamelCase )
lowerCamelCase__ : List[Any] = split_and_add(_lowerCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 696 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = tempfile.mkdtemp()
# fmt: off
lowerCamelCase__ : int = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
lowerCamelCase__ : Union[str, Any] = dict(zip(lowerCamelCase_, range(len(lowerCamelCase_ ) ) ) )
lowerCamelCase__ : Any = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
lowerCamelCase__ : str = {'unk_token': '<unk>'}
lowerCamelCase__ : Dict = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase__ : str = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file, 'w', encoding='utf-8' ) as fp:
fp.write(json.dumps(lowerCamelCase_ ) + '\n' )
with open(self.merges_file, 'w', encoding='utf-8' ) as fp:
fp.write('\n'.join(lowerCamelCase_ ) )
lowerCamelCase__ : Union[str, Any] = {
'do_resize': True,
'size': 2_0,
'do_center_crop': True,
'crop_size': 1_8,
'do_normalize': True,
'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowerCamelCase__ : Tuple = os.path.join(self.tmpdirname, lowerCamelCase_ )
with open(self.image_processor_file, 'w', encoding='utf-8' ) as fp:
json.dump(lowerCamelCase_, lowerCamelCase_ )
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname, **lowerCamelCase_ )
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **lowerCamelCase_ )
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
return CLIPImageProcessor.from_pretrained(self.tmpdirname, **lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = [np.random.randint(2_5_5, size=(3, 3_0, 4_0_0), dtype=np.uinta )]
lowerCamelCase__ : Tuple = [Image.fromarray(np.moveaxis(lowerCamelCase_, 0, -1 ) ) for x in image_inputs]
return image_inputs
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = self.get_tokenizer()
lowerCamelCase__ : Optional[int] = self.get_rust_tokenizer()
lowerCamelCase__ : Any = self.get_image_processor()
lowerCamelCase__ : Dict = CLIPProcessor(tokenizer=lowerCamelCase_, image_processor=lowerCamelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
lowerCamelCase__ : Dict = CLIPProcessor.from_pretrained(self.tmpdirname, use_fast=lowerCamelCase_ )
lowerCamelCase__ : List[Any] = CLIPProcessor(tokenizer=lowerCamelCase_, image_processor=lowerCamelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
lowerCamelCase__ : Optional[int] = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer, lowerCamelCase_ )
self.assertIsInstance(processor_fast.tokenizer, lowerCamelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor, lowerCamelCase_ )
self.assertIsInstance(processor_fast.image_processor, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = CLIPProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ : List[Any] = self.get_tokenizer(bos_token='(BOS)', eos_token='(EOS)' )
lowerCamelCase__ : Optional[Any] = self.get_image_processor(do_normalize=lowerCamelCase_, padding_value=1.0 )
lowerCamelCase__ : int = CLIPProcessor.from_pretrained(
self.tmpdirname, bos_token='(BOS)', eos_token='(EOS)', do_normalize=lowerCamelCase_, padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, lowerCamelCase_ )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.get_image_processor()
lowerCamelCase__ : Any = self.get_tokenizer()
lowerCamelCase__ : List[str] = CLIPProcessor(tokenizer=lowerCamelCase_, image_processor=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = self.prepare_image_inputs()
lowerCamelCase__ : int = image_processor(lowerCamelCase_, return_tensors='np' )
lowerCamelCase__ : Optional[Any] = processor(images=lowerCamelCase_, return_tensors='np' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2 )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.get_image_processor()
lowerCamelCase__ : str = self.get_tokenizer()
lowerCamelCase__ : Optional[Any] = CLIPProcessor(tokenizer=lowerCamelCase_, image_processor=lowerCamelCase_ )
lowerCamelCase__ : List[Any] = 'lower newer'
lowerCamelCase__ : Optional[int] = processor(text=lowerCamelCase_ )
lowerCamelCase__ : Dict = tokenizer(lowerCamelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.get_image_processor()
lowerCamelCase__ : Tuple = self.get_tokenizer()
lowerCamelCase__ : List[Any] = CLIPProcessor(tokenizer=lowerCamelCase_, image_processor=lowerCamelCase_ )
lowerCamelCase__ : str = 'lower newer'
lowerCamelCase__ : Optional[Any] = self.prepare_image_inputs()
lowerCamelCase__ : Tuple = processor(text=lowerCamelCase_, images=lowerCamelCase_ )
self.assertListEqual(list(inputs.keys() ), ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase_ ):
processor()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.get_image_processor()
lowerCamelCase__ : Optional[int] = self.get_tokenizer()
lowerCamelCase__ : Tuple = CLIPProcessor(tokenizer=lowerCamelCase_, image_processor=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCamelCase__ : Union[str, Any] = processor.batch_decode(lowerCamelCase_ )
lowerCamelCase__ : Tuple = tokenizer.batch_decode(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.get_image_processor()
lowerCamelCase__ : str = self.get_tokenizer()
lowerCamelCase__ : int = CLIPProcessor(tokenizer=lowerCamelCase_, image_processor=lowerCamelCase_ )
lowerCamelCase__ : str = 'lower newer'
lowerCamelCase__ : List[Any] = self.prepare_image_inputs()
lowerCamelCase__ : Dict = processor(text=lowerCamelCase_, images=lowerCamelCase_ )
self.assertListEqual(list(inputs.keys() ), processor.model_input_names )
| 696 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A_ : Dict = "pt"
elif is_tf_available():
A_ : Union[str, Any] = "tf"
else:
A_ : List[str] = "jax"
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = PerceiverTokenizer
lowerCamelCase__ : Optional[Any] = False
def a__ (self ):
'''simple docstring'''
super().setUp()
lowerCamelCase__ : int = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def a__ (self ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver' )
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname, **lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_=False, lowerCamelCase_=2_0, lowerCamelCase_=5 ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = []
for i in range(len(lowerCamelCase_ ) ):
try:
lowerCamelCase__ : Any = tokenizer.decode([i], clean_up_tokenization_spaces=lowerCamelCase_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCamelCase__ : Any = list(filter(lambda lowerCamelCase_ : re.match(r'^[ a-zA-Z]+$', t[1] ), lowerCamelCase_ ) )
lowerCamelCase__ : Union[str, Any] = list(filter(lambda lowerCamelCase_ : [t[0]] == tokenizer.encode(t[1], add_special_tokens=lowerCamelCase_ ), lowerCamelCase_ ) )
if max_length is not None and len(lowerCamelCase_ ) > max_length:
lowerCamelCase__ : int = toks[:max_length]
if min_length is not None and len(lowerCamelCase_ ) < min_length and len(lowerCamelCase_ ) > 0:
while len(lowerCamelCase_ ) < min_length:
lowerCamelCase__ : Dict = toks + toks
# toks_str = [t[1] for t in toks]
lowerCamelCase__ : int = [t[0] for t in toks]
# Ensure consistency
lowerCamelCase__ : Optional[int] = tokenizer.decode(lowerCamelCase_, clean_up_tokenization_spaces=lowerCamelCase_ )
if " " not in output_txt and len(lowerCamelCase_ ) > 1:
lowerCamelCase__ : List[Any] = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=lowerCamelCase_ )
+ ' '
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=lowerCamelCase_ )
)
if with_prefix_space:
lowerCamelCase__ : Optional[Any] = ' ' + output_txt
lowerCamelCase__ : List[Any] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
return output_txt, output_ids
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = 'Unicode €.'
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_ )
lowerCamelCase__ : Dict = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : int = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]Unicode €.[SEP]' )
lowerCamelCase__ : List[str] = tokenizer('e è é ê ë' )
lowerCamelCase__ : Dict = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : Any = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]e è é ê ë[SEP]' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ), '[CLS]e è é ê ë[SEP]' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
lowerCamelCase__ : List[Any] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
if FRAMEWORK != "jax":
lowerCamelCase__ : List[str] = list(batch.input_ids.numpy()[0] )
else:
lowerCamelCase__ : int = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertEqual((2, 3_8), batch.input_ids.shape )
self.assertEqual((2, 3_8), batch.attention_mask.shape )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.perceiver_tokenizer
lowerCamelCase__ : List[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCamelCase__ : List[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids', lowerCamelCase_ )
self.assertIn('attention_mask', lowerCamelCase_ )
self.assertNotIn('decoder_input_ids', lowerCamelCase_ )
self.assertNotIn('decoder_attention_mask', lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : int = [
'Summary of the text.',
'Another summary.',
]
lowerCamelCase__ : str = tokenizer(
text_target=lowerCamelCase_, max_length=3_2, padding='max_length', truncation=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertEqual(3_2, targets['input_ids'].shape[1] )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length, 4_2 )
# Now let's start the test
lowerCamelCase__ : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : str = ' He is very happy, UNwant\u00E9d,running'
lowerCamelCase__ : str = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : str = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
shutil.rmtree(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : Union[str, Any] = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
lowerCamelCase__ : List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
lowerCamelCase__ : List[str] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : int = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Tuple = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertIn('new_additional_special_token', after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length, 4_2 )
lowerCamelCase__ : List[Any] = tokenizer.__class__.from_pretrained(lowerCamelCase_, model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length, 4_3 )
shutil.rmtree(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : Optional[Any] = json.load(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : List[str] = json.load(lowerCamelCase_ )
lowerCamelCase__ : Any = [f'''<extra_id_{i}>''' for i in range(1_2_5 )]
lowerCamelCase__ : Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
lowerCamelCase__ : List[str] = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCamelCase__ : Dict = tokenizer_class.from_pretrained(
lowerCamelCase_, )
self.assertIn(
'an_additional_special_token', tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['an_additional_special_token'], tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ), )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCamelCase__ : Optional[Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token', lstrip=lowerCamelCase_ )]
lowerCamelCase__ : Any = tokenizer_class.from_pretrained(
lowerCamelCase_, additional_special_tokens=lowerCamelCase_, )
self.assertIn('a_new_additional_special_token', tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'], tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ), )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ), '�' )
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.get_tokenizers(fast=lowerCamelCase_, do_lower_case=lowerCamelCase_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase__ : Tuple = ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
lowerCamelCase__ : List[str] = tokenizer.convert_tokens_to_string(lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
A_ : Optional[int] = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
A_ : List[str] = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
A_ : int = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'predictions': datasets.Value('string', id='sequence' ),
'references': datasets.Value('string', id='sequence' ),
} ), reference_urls=[], )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=False, lowerCamelCase_=False, lowerCamelCase_=False, ):
'''simple docstring'''
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
lowerCamelCase__ : Dict = np.array([re.sub(lowerCamelCase_, '', lowerCamelCase_ ) for x in predictions] )
lowerCamelCase__ : List[Any] = np.array([re.sub(lowerCamelCase_, '', lowerCamelCase_ ) for x in references] )
else:
lowerCamelCase__ : int = np.asarray(lowerCamelCase_ )
lowerCamelCase__ : str = np.asarray(lowerCamelCase_ )
if ignore_case:
lowerCamelCase__ : Optional[Any] = np.char.lower(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = np.char.lower(lowerCamelCase_ )
if ignore_punctuation:
lowerCamelCase__ : Dict = string.punctuation.maketrans('', '', string.punctuation )
lowerCamelCase__ : Any = np.char.translate(lowerCamelCase_, table=lowerCamelCase_ )
lowerCamelCase__ : str = np.char.translate(lowerCamelCase_, table=lowerCamelCase_ )
if ignore_numbers:
lowerCamelCase__ : Optional[int] = string.digits.maketrans('', '', string.digits )
lowerCamelCase__ : Union[str, Any] = np.char.translate(lowerCamelCase_, table=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = np.char.translate(lowerCamelCase_, table=lowerCamelCase_ )
lowerCamelCase__ : Any = predictions == references
return {"exact_match": np.mean(lowerCamelCase_ ) * 1_0_0}
| 696 |
"""simple docstring"""
from math import pi, sqrt, tan
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
lowerCamelCase__ : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_lowerCamelCase , 2 ) * torus_radius * tube_radius
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
lowerCamelCase__ : Dict = (sidea + sidea + sidea) / 2
lowerCamelCase__ : str = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 696 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : List[Any] = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : int = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[str] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
A_ : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=9_9, lowerCamelCase_=6_4, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Dict = parent
lowerCamelCase__ : Tuple = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : List[Any] = is_training
lowerCamelCase__ : str = use_input_mask
lowerCamelCase__ : Optional[Any] = use_token_type_ids
lowerCamelCase__ : Any = use_labels
lowerCamelCase__ : Optional[int] = vocab_size
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : Optional[int] = num_hidden_layers
lowerCamelCase__ : List[Any] = num_attention_heads
lowerCamelCase__ : Union[str, Any] = intermediate_size
lowerCamelCase__ : List[str] = hidden_act
lowerCamelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCamelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCamelCase__ : Dict = max_position_embeddings
lowerCamelCase__ : Dict = type_vocab_size
lowerCamelCase__ : Union[str, Any] = type_sequence_label_size
lowerCamelCase__ : List[Any] = initializer_range
lowerCamelCase__ : List[Any] = num_labels
lowerCamelCase__ : Union[str, Any] = num_choices
lowerCamelCase__ : List[str] = scope
lowerCamelCase__ : Dict = vocab_size - 1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Optional[Any] = None
if self.use_input_mask:
lowerCamelCase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Any = None
if self.use_labels:
lowerCamelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : str = self.get_config()
return config, input_ids, input_mask, token_labels
def a__ (self ):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=lowerCamelCase_, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[str] = self.prepare_config_and_inputs()
lowerCamelCase__ : Optional[Any] = True
return config, input_ids, input_mask, token_labels
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = GPTNeoXModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.num_labels
lowerCamelCase__ : Optional[Any] = GPTNeoXForQuestionAnswering(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : str = self.num_labels
lowerCamelCase__ : Optional[int] = GPTNeoXForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : List[Any] = GPTNeoXForTokenClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Tuple = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = True
lowerCamelCase__ : List[str] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
# first forward pass
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, use_cache=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase__ : str = ids_tensor((self.batch_size, 3), config.vocab_size )
lowerCamelCase__ : List[Any] = ids_tensor((self.batch_size, 3), vocab_size=2 )
# append to next input_ids and
lowerCamelCase__ : Tuple = torch.cat([input_ids, next_tokens], dim=-1 )
lowerCamelCase__ : Tuple = torch.cat([input_mask, next_mask], dim=-1 )
lowerCamelCase__ : List[str] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, output_hidden_states=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = output_from_no_past['hidden_states'][0]
lowerCamelCase__ : Optional[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, past_key_values=lowerCamelCase_, output_hidden_states=lowerCamelCase_, )['hidden_states'][0]
# select random slice
lowerCamelCase__ : Dict = ids_tensor((1,), output_from_past.shape[-1] ).item()
lowerCamelCase__ : Optional[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-3 ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = config_and_inputs
lowerCamelCase__ : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : int = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ : Dict = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Optional[int] = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = GPTNeoXModelTester(self )
lowerCamelCase__ : Union[str, Any] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=6_4, num_attention_heads=8 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCamelCase__ : Optional[Any] = None
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
@unittest.skip(reason='Feed forward chunking is not implemented' )
def a__ (self ):
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Optional[Any] = ids_tensor([1, 1_0], config.vocab_size )
lowerCamelCase__ : Tuple = ids_tensor([1, int(config.max_position_embeddings * 1.5 )], config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Any = GPTNeoXModel(lowerCamelCase_ )
original_model.to(lowerCamelCase_ )
original_model.eval()
lowerCamelCase__ : List[Any] = original_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = original_model(lowerCamelCase_ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Optional[int] = {'type': scaling_type, 'factor': 10.0}
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
scaled_model.to(lowerCamelCase_ )
scaled_model.eval()
lowerCamelCase__ : Tuple = scaled_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = scaled_model(lowerCamelCase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
@require_torch
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' )
for checkpointing in [True, False]:
lowerCamelCase__ : Optional[Any] = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = tokenizer('My favorite food is', return_tensors='pt' ).to(lowerCamelCase_ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
lowerCamelCase__ : Dict = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
lowerCamelCase__ : Dict = model.generate(**lowerCamelCase_, do_sample=lowerCamelCase_, max_new_tokens=2_0 )
lowerCamelCase__ : Optional[Any] = tokenizer.batch_decode(lowerCamelCase_ )[0]
self.assertEqual(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
import numpy
# List of input, output pairs
A_ : Tuple = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
A_ : List[Any] = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
A_ : int = [2, 4, 1, 5]
A_ : Optional[int] = len(train_data)
A_ : int = 0.009
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase="train" ):
return calculate_hypothesis_value(_lowerCamelCase , _lowerCamelCase ) - output(
_lowerCamelCase , _lowerCamelCase )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[int] = 0
for i in range(len(_lowerCamelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase=m ):
lowerCamelCase__ : List[str] = 0
for i in range(_lowerCamelCase ):
if index == -1:
summation_value += _error(_lowerCamelCase )
else:
summation_value += _error(_lowerCamelCase ) * train_data[i][0][index]
return summation_value
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : str = summation_of_cost_derivative(_lowerCamelCase , _lowerCamelCase ) / m
return cost_derivative_value
def lowerCamelCase_ ( ):
global parameter_vector
# Tune these values to set a tolerance value for predicted output
lowerCamelCase__ : Optional[Any] = 0.000_002
lowerCamelCase__ : str = 0
lowerCamelCase__ : int = 0
while True:
j += 1
lowerCamelCase__ : Optional[Any] = [0, 0, 0, 0]
for i in range(0 , len(_lowerCamelCase ) ):
lowerCamelCase__ : List[Any] = get_cost_derivative(i - 1 )
lowerCamelCase__ : str = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
_lowerCamelCase , _lowerCamelCase , atol=_lowerCamelCase , rtol=_lowerCamelCase , ):
break
lowerCamelCase__ : Tuple = temp_parameter_vector
print(('Number of iterations:', j) )
def lowerCamelCase_ ( ):
for i in range(len(_lowerCamelCase ) ):
print(('Actual output value:', output(_lowerCamelCase , 'test' )) )
print(('Hypothesis output:', calculate_hypothesis_value(_lowerCamelCase , 'test' )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 696 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A_ : Dict = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
A_ : List[Any] = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
A_ : Union[str, Any] = spec.loader.load_module()
A_ : int = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
A_ : Optional[int] = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
A_ : str = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def lowerCamelCase_ ( ):
lowerCamelCase__ : Dict = []
for config_class in list(CONFIG_MAPPING.values() ):
lowerCamelCase__ : Dict = False
# source code of `config_class`
lowerCamelCase__ : str = inspect.getsource(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = _re_checkpoint.findall(_lowerCamelCase )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
lowerCamelCase__ : Any = f'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
lowerCamelCase__ : Any = True
break
lowerCamelCase__ : Dict = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
lowerCamelCase__ : Optional[Any] = '\n'.join(sorted(_lowerCamelCase ) )
raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 696 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A_ : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Any = ["DPTFeatureExtractor"]
A_ : Optional[Any] = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[str] = [
"DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DPTForDepthEstimation",
"DPTForSemanticSegmentation",
"DPTModel",
"DPTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A_ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = ["LlamaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = ["LlamaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"LlamaForCausalLM",
"LlamaModel",
"LlamaPreTrainedModel",
"LlamaForSequenceClassification",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
A_ : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 | 1 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_ = None, lowerCamelCase_ = None, lowerCamelCase_ = False, lowerCamelCase_ = False, lowerCamelCase_ = None, lowerCamelCase_ = None, **lowerCamelCase_, ):
'''simple docstring'''
super().__init__(
features=lowerCamelCase_, cache_dir=lowerCamelCase_, keep_in_memory=lowerCamelCase_, streaming=lowerCamelCase_, num_proc=lowerCamelCase_, **lowerCamelCase_, )
lowerCamelCase__ : int = Generator(
cache_dir=lowerCamelCase_, features=lowerCamelCase_, generator=lowerCamelCase_, gen_kwargs=lowerCamelCase_, **lowerCamelCase_, )
def a__ (self ):
'''simple docstring'''
if self.streaming:
lowerCamelCase__ : Dict = self.builder.as_streaming_dataset(split='train' )
# Build regular (map-style) dataset
else:
lowerCamelCase__ : Optional[Any] = None
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : Optional[Any] = None
lowerCamelCase__ : str = None
self.builder.download_and_prepare(
download_config=lowerCamelCase_, download_mode=lowerCamelCase_, verification_mode=lowerCamelCase_, base_path=lowerCamelCase_, num_proc=self.num_proc, )
lowerCamelCase__ : str = self.builder.as_dataset(
split='train', verification_mode=lowerCamelCase_, in_memory=self.keep_in_memory )
return dataset
| 696 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A_ : Optional[int] = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A_ : List[str] = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
for data in res.iter_content(1_00_00):
out_file.write(data)
A_ : Tuple = BeautifulSoup(res.text, "html.parser")
A_ : Dict = list(soup.select(".eZt8xd"))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("href"))
else:
webbrowser.open(f"https://google.com{link.get('href')}")
| 696 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ : Any = (7_20, 12_80) # Height, Width
A_ : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ : int = 1 / 1_00
A_ : Dict = ""
A_ : List[str] = ""
A_ : str = ""
A_ : int = 2_50
def lowerCamelCase_ ( ):
lowerCamelCase__ , lowerCamelCase__ : Tuple = get_dataset(_lowerCamelCase , _lowerCamelCase )
for index in range(_lowerCamelCase ):
lowerCamelCase__ : Dict = random.sample(range(len(_lowerCamelCase ) ) , 4 )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = update_image_and_anno(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , filter_scale=_lowerCamelCase , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
lowerCamelCase__ : int = random_chars(32 )
lowerCamelCase__ : Optional[Any] = path.split(os.sep )[-1].rsplit('.' , 1 )[0]
lowerCamelCase__ : Optional[Any] = f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
cva.imwrite(f'''{file_root}.jpg''' , _lowerCamelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}''' )
lowerCamelCase__ : Union[str, Any] = []
for anno in new_annos:
lowerCamelCase__ : List[Any] = anno[3] - anno[1]
lowerCamelCase__ : Any = anno[4] - anno[2]
lowerCamelCase__ : Any = anno[1] + width / 2
lowerCamelCase__ : List[Any] = anno[2] + height / 2
lowerCamelCase__ : List[str] = f'''{anno[0]} {x_center} {y_center} {width} {height}'''
annos_list.append(_lowerCamelCase )
with open(f'''{file_root}.txt''' , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = []
lowerCamelCase__ : Dict = []
for label_file in glob.glob(os.path.join(_lowerCamelCase , '*.txt' ) ):
lowerCamelCase__ : Union[str, Any] = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(_lowerCamelCase ) as in_file:
lowerCamelCase__ : List[Any] = in_file.readlines()
lowerCamelCase__ : Tuple = os.path.join(_lowerCamelCase , f'''{label_name}.jpg''' )
lowerCamelCase__ : Optional[Any] = []
for obj_list in obj_lists:
lowerCamelCase__ : List[Any] = obj_list.rstrip('\n' ).split(' ' )
lowerCamelCase__ : str = float(obj[1] ) - float(obj[3] ) / 2
lowerCamelCase__ : Optional[int] = float(obj[2] ) - float(obj[4] ) / 2
lowerCamelCase__ : Optional[int] = float(obj[1] ) + float(obj[3] ) / 2
lowerCamelCase__ : int = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(_lowerCamelCase )
labels.append(_lowerCamelCase )
return img_paths, labels
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0.0 , ):
lowerCamelCase__ : str = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
lowerCamelCase__ : List[Any] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
lowerCamelCase__ : Optional[Any] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
lowerCamelCase__ : Any = int(scale_x * output_size[1] )
lowerCamelCase__ : Dict = int(scale_y * output_size[0] )
lowerCamelCase__ : int = []
lowerCamelCase__ : Tuple = []
for i, index in enumerate(_lowerCamelCase ):
lowerCamelCase__ : Any = all_img_list[index]
path_list.append(_lowerCamelCase )
lowerCamelCase__ : Dict = all_annos[index]
lowerCamelCase__ : Tuple = cva.imread(_lowerCamelCase )
if i == 0: # top-left
lowerCamelCase__ : int = cva.resize(_lowerCamelCase , (divid_point_x, divid_point_y) )
lowerCamelCase__ : Dict = img
for bbox in img_annos:
lowerCamelCase__ : List[Any] = bbox[1] * scale_x
lowerCamelCase__ : str = bbox[2] * scale_y
lowerCamelCase__ : Optional[int] = bbox[3] * scale_x
lowerCamelCase__ : Any = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
lowerCamelCase__ : Tuple = cva.resize(_lowerCamelCase , (output_size[1] - divid_point_x, divid_point_y) )
lowerCamelCase__ : Optional[int] = img
for bbox in img_annos:
lowerCamelCase__ : Optional[int] = scale_x + bbox[1] * (1 - scale_x)
lowerCamelCase__ : Optional[Any] = bbox[2] * scale_y
lowerCamelCase__ : Optional[int] = scale_x + bbox[3] * (1 - scale_x)
lowerCamelCase__ : Optional[Any] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
lowerCamelCase__ : List[str] = cva.resize(_lowerCamelCase , (divid_point_x, output_size[0] - divid_point_y) )
lowerCamelCase__ : int = img
for bbox in img_annos:
lowerCamelCase__ : Any = bbox[1] * scale_x
lowerCamelCase__ : Optional[Any] = scale_y + bbox[2] * (1 - scale_y)
lowerCamelCase__ : Optional[int] = bbox[3] * scale_x
lowerCamelCase__ : str = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
lowerCamelCase__ : Union[str, Any] = cva.resize(
_lowerCamelCase , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
lowerCamelCase__ : Optional[int] = img
for bbox in img_annos:
lowerCamelCase__ : List[Any] = scale_x + bbox[1] * (1 - scale_x)
lowerCamelCase__ : Any = scale_y + bbox[2] * (1 - scale_y)
lowerCamelCase__ : int = scale_x + bbox[3] * (1 - scale_x)
lowerCamelCase__ : Tuple = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
lowerCamelCase__ : List[Any] = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def lowerCamelCase_ ( _lowerCamelCase ):
assert number_char > 1, "The number of character should greater than 1"
lowerCamelCase__ : Any = ascii_lowercase + digits
return "".join(random.choice(_lowerCamelCase ) for _ in range(_lowerCamelCase ) )
if __name__ == "__main__":
main()
print("DONE ✅")
| 696 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
lowerCamelCase__ : Tuple = FlaxDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=lowerCamelCase_, cache_dir=lowerCamelCase_ )
lowerCamelCase__ : List[str] = [t[-1] for t in os.walk(os.path.join(lowerCamelCase_, os.listdir(lowerCamelCase_ )[0], 'snapshots' ) )]
lowerCamelCase__ : Optional[int] = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('.bin' ) for f in files )
@slow
@require_flax
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Any = FlaxStableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Any = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Optional[int] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Any = 4
lowerCamelCase__ : Any = jax.device_count()
lowerCamelCase__ : List[Any] = num_samples * [prompt]
lowerCamelCase__ : Optional[int] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : int = replicate(lowerCamelCase_ )
lowerCamelCase__ : Any = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : int = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 4.1_514_745 ) < 1e-3
assert np.abs(np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 49_947.875 ) < 5e-1
lowerCamelCase__ : Union[str, Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(lowerCamelCase_ ) == num_samples
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='flax', safety_checker=lowerCamelCase_ )
lowerCamelCase__ : int = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : List[str] = jax.random.PRNGKey(0 )
lowerCamelCase__ : int = 5_0
lowerCamelCase__ : List[str] = jax.device_count()
lowerCamelCase__ : Dict = num_samples * [prompt]
lowerCamelCase__ : List[str] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Dict = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = shard(lowerCamelCase_ )
lowerCamelCase__ : str = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.05_652_401) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_383_808.2) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : List[Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Union[str, Any] = 5_0
lowerCamelCase__ : Any = jax.device_count()
lowerCamelCase__ : Tuple = num_samples * [prompt]
lowerCamelCase__ : List[str] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Any = replicate(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : int = shard(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.04_003_906) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_373_516.75) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Tuple = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa )
lowerCamelCase__ : Tuple = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Union[str, Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Optional[Any] = 5_0
lowerCamelCase__ : Tuple = jax.device_count()
lowerCamelCase__ : Optional[int] = num_samples * [prompt]
lowerCamelCase__ : str = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Optional[int] = replicate(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : List[str] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.04_003_906) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_373_516.75) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = FlaxDDIMScheduler(
beta_start=0.00_085, beta_end=0.012, beta_schedule='scaled_linear', set_alpha_to_one=lowerCamelCase_, steps_offset=1, )
lowerCamelCase__ , lowerCamelCase__ : List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, scheduler=lowerCamelCase_, safety_checker=lowerCamelCase_, )
lowerCamelCase__ : List[str] = scheduler.create_state()
lowerCamelCase__ : int = scheduler_state
lowerCamelCase__ : Any = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Optional[Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : int = 5_0
lowerCamelCase__ : Optional[Any] = jax.device_count()
lowerCamelCase__ : Any = num_samples * [prompt]
lowerCamelCase__ : Any = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Union[str, Any] = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Dict = shard(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.045_043_945) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_347_693.5) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : int = jax.device_count()
lowerCamelCase__ : Dict = num_samples * [prompt]
lowerCamelCase__ : str = jax.random.split(jax.random.PRNGKey(0 ), lowerCamelCase_ )
lowerCamelCase__ , lowerCamelCase__ : List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_, )
lowerCamelCase__ : Union[str, Any] = replicate(lowerCamelCase_ )
lowerCamelCase__ : Dict = pipeline.prepare_inputs(lowerCamelCase_ )
lowerCamelCase__ : Tuple = shard(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : int = images[2, 0, 2_5_6, 1_0:1_7, 1]
# With memory efficient attention
lowerCamelCase__ , lowerCamelCase__ : str = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_, use_memory_efficient_attention=lowerCamelCase_, )
lowerCamelCase__ : Dict = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = pipeline.prepare_inputs(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : Any = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : Any = images[2, 0, 2_5_6, 1_0:1_7, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1e-2
| 696 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Any = {
"configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = [
"LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
"LiltForQuestionAnswering",
"LiltForSequenceClassification",
"LiltForTokenClassification",
"LiltModel",
"LiltPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
A_ : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A_ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowerCamelCase_, scheduler=lowerCamelCase_ )
@torch.no_grad()
def __call__(self, lowerCamelCase_ = 1, lowerCamelCase_ = 1_0_0, lowerCamelCase_ = None, lowerCamelCase_ = None, lowerCamelCase_ = True, ):
'''simple docstring'''
if audio_length_in_s is None:
lowerCamelCase__ : str = self.unet.config.sample_size / self.unet.config.sample_rate
lowerCamelCase__ : Optional[Any] = audio_length_in_s * self.unet.config.sample_rate
lowerCamelCase__ : str = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowerCamelCase__ : Dict = int(lowerCamelCase_ )
if sample_size % down_scale_factor != 0:
lowerCamelCase__ : Union[str, Any] = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
' process.' )
lowerCamelCase__ : Optional[Any] = int(lowerCamelCase_ )
lowerCamelCase__ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowerCamelCase__ : Union[str, Any] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowerCamelCase_, lowerCamelCase_ ) and len(lowerCamelCase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowerCamelCase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowerCamelCase__ : Union[str, Any] = randn_tensor(lowerCamelCase_, generator=lowerCamelCase_, device=self.device, dtype=lowerCamelCase_ )
# set step values
self.scheduler.set_timesteps(lowerCamelCase_, device=audio.device )
lowerCamelCase__ : int = self.scheduler.timesteps.to(lowerCamelCase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowerCamelCase__ : List[Any] = self.unet(lowerCamelCase_, lowerCamelCase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowerCamelCase__ : List[str] = self.scheduler.step(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ).prev_sample
lowerCamelCase__ : Union[str, Any] = audio.clamp(-1, 1 ).float().cpu().numpy()
lowerCamelCase__ : Tuple = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[str] = np.max(_lowerCamelCase , axis=-1 , keepdims=_lowerCamelCase )
lowerCamelCase__ : List[Any] = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=_lowerCamelCase )
class a_ ( snake_case_ ):
'''simple docstring'''
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = {}
if "second_text" in kwargs:
lowerCamelCase__ : Tuple = kwargs['second_text']
return preprocess_kwargs, {}, {}
def a__ (self, lowerCamelCase_, lowerCamelCase_=None ):
'''simple docstring'''
return self.tokenizer(lowerCamelCase_, text_pair=lowerCamelCase_, return_tensors=self.framework )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
return self.model(**lowerCamelCase_ )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = model_outputs.logits[0].numpy()
lowerCamelCase__ : List[str] = softmax(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = np.argmax(lowerCamelCase_ )
lowerCamelCase__ : str = self.model.config.idalabel[best_class]
lowerCamelCase__ : List[str] = probabilities[best_class].item()
lowerCamelCase__ : Union[str, Any] = logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 696 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class a_ :
'''simple docstring'''
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return None
class a_ :
'''simple docstring'''
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return None
class a_ ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase_, 'tf', 1_2, **lowerCamelCase_ )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase_, 'pt', 1_2, **lowerCamelCase_ )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
from transformers import BertModel
lowerCamelCase__ : Union[str, Any] = ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
with NamedTemporaryFile(mode='w+t' ) as vocab_file:
vocab_file.write('\n'.join(lowerCamelCase_ ) )
vocab_file.flush()
lowerCamelCase__ : Tuple = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowerCamelCase__ : Optional[Any] = BertModel(BertConfig(vocab_size=len(lowerCamelCase_ ) ) )
model.save_pretrained(lowerCamelCase_ )
self._test_export(lowerCamelCase_, 'pt', 1_2, lowerCamelCase_ )
@require_tf
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCamelCase__ : Optional[Any] = self._test_export(lowerCamelCase_, 'tf', 1_2, **lowerCamelCase_ )
lowerCamelCase__ : Any = quantize(Path(lowerCamelCase_ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase_ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCamelCase__ : Any = self._test_export(lowerCamelCase_, 'pt', 1_2, **lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = quantize(lowerCamelCase_ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase_ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, **lowerCamelCase_ ):
'''simple docstring'''
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowerCamelCase__ : str = Path(lowerCamelCase_ ).joinpath('model.onnx' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ )
return path
except Exception as e:
self.fail(lowerCamelCase_ )
@require_torch
@require_tokenizers
@slow
def a__ (self ):
'''simple docstring'''
from transformers import BertModel
lowerCamelCase__ : str = BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowerCamelCase__ : Union[str, Any] = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(lowerCamelCase_, lowerCamelCase_, 'pt' )
@require_tf
@require_tokenizers
@slow
def a__ (self ):
'''simple docstring'''
from transformers import TFBertModel
lowerCamelCase__ : Dict = TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowerCamelCase__ : Optional[int] = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(lowerCamelCase_, lowerCamelCase_, 'tf' )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = FeatureExtractionPipeline(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = infer_shapes(lowerCamelCase_, lowerCamelCase_ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase_ ), len(lowerCamelCase_ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3], lowerCamelCase_ )
self.assertSequenceEqual(variable_names[3:], lowerCamelCase_ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name], {0: 'batch', 1: 'sequence'} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['output_0'], {0: 'batch', 1: 'sequence'} )
self.assertDictEqual(shapes['output_1'], {0: 'batch'} )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = ['input_ids', 'attention_mask', 'token_type_ids']
lowerCamelCase__ : Optional[int] = {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
lowerCamelCase__ , lowerCamelCase__ : str = ensure_valid_input(FuncContiguousArgs(), lowerCamelCase_, lowerCamelCase_ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase_ ), 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase_ ), set(lowerCamelCase_ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase_, (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowerCamelCase__ , lowerCamelCase__ : Any = ensure_valid_input(FuncNonContiguousArgs(), lowerCamelCase_, lowerCamelCase_ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase_ ), 1 )
self.assertEqual(len(lowerCamelCase_ ), 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0], tokens['input_ids'] )
self.assertEqual(ordered_input_names[0], 'input_ids' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = generate_identified_filename(Path('/home/something/my_fake_model.onnx' ), '-test' )
self.assertEqual('/home/something/my_fake_model-test.onnx', generated.as_posix() )
| 696 | 1 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : torch.FloatTensor
lowerCamelCase__ : Optional[torch.FloatTensor] = None
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase=0.999 , _lowerCamelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(_lowerCamelCase ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_lowerCamelCase ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
lowerCamelCase__ : Optional[Any] = []
for i in range(_lowerCamelCase ):
lowerCamelCase__ : int = i / num_diffusion_timesteps
lowerCamelCase__ : Dict = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_lowerCamelCase ) / alpha_bar_fn(_lowerCamelCase ) , _lowerCamelCase ) )
return torch.tensor(_lowerCamelCase , dtype=torch.floataa )
class a_ ( snake_case_ , snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = 1
@register_to_config
def __init__(self, lowerCamelCase_ = 1_0_0_0, lowerCamelCase_ = 0.0_001, lowerCamelCase_ = 0.02, lowerCamelCase_ = "linear", lowerCamelCase_ = None, lowerCamelCase_ = True, lowerCamelCase_ = True, lowerCamelCase_ = 0, lowerCamelCase_ = "epsilon", lowerCamelCase_ = 1.0, **lowerCamelCase_, ):
'''simple docstring'''
if kwargs.get('set_alpha_to_one', lowerCamelCase_ ) is not None:
lowerCamelCase__ : Union[str, Any] = (
'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
)
deprecate('set_alpha_to_one', '1.0.0', lowerCamelCase_, standard_warn=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = kwargs['set_alpha_to_one']
if trained_betas is not None:
lowerCamelCase__ : Union[str, Any] = torch.tensor(lowerCamelCase_, dtype=torch.floataa )
elif beta_schedule == "linear":
lowerCamelCase__ : Dict = torch.linspace(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowerCamelCase__ : Optional[int] = (
torch.linspace(beta_start**0.5, beta_end**0.5, lowerCamelCase_, dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowerCamelCase__ : Dict = betas_for_alpha_bar(lowerCamelCase_ )
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )
lowerCamelCase__ : Tuple = 1.0 - self.betas
lowerCamelCase__ : Optional[Any] = torch.cumprod(self.alphas, dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
lowerCamelCase__ : Tuple = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
lowerCamelCase__ : List[Any] = 1.0
# setable values
lowerCamelCase__ : List[str] = None
lowerCamelCase__ : str = torch.from_numpy(np.arange(0, lowerCamelCase_ ).copy().astype(np.intaa ) )
def a__ (self, lowerCamelCase_, lowerCamelCase_ = None ):
'''simple docstring'''
return sample
def a__ (self, lowerCamelCase_, lowerCamelCase_ = None ):
'''simple docstring'''
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
f''' maximal {self.config.num_train_timesteps} timesteps.''' )
lowerCamelCase__ : List[str] = num_inference_steps
lowerCamelCase__ : Union[str, Any] = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCamelCase__ : List[str] = (np.arange(0, lowerCamelCase_ ) * step_ratio).round().copy().astype(np.intaa )
lowerCamelCase__ : Dict = torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )
self.timesteps += self.config.steps_offset
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ = 0.0, lowerCamelCase_ = False, lowerCamelCase_ = None, lowerCamelCase_ = True, ):
'''simple docstring'''
lowerCamelCase__ : str = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
lowerCamelCase__ : Union[str, Any] = self.alphas_cumprod[timestep]
lowerCamelCase__ : Tuple = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
lowerCamelCase__ : Dict = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
lowerCamelCase__ : str = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
lowerCamelCase__ : int = model_output
elif self.config.prediction_type == "sample":
lowerCamelCase__ : Optional[int] = model_output
lowerCamelCase__ : int = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
lowerCamelCase__ : List[str] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
lowerCamelCase__ : Dict = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
' `v_prediction`' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
lowerCamelCase__ : Tuple = pred_original_sample.clamp(
-self.config.clip_sample_range, self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCamelCase__ : Optional[Any] = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCamelCase__ : Optional[int] = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=lowerCamelCase_, pred_original_sample=lowerCamelCase_ )
def __len__(self ):
'''simple docstring'''
return self.config.num_train_timesteps
| 696 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : int = KandinskyVaaControlnetImgaImgPipeline
lowerCamelCase__ : Optional[int] = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
lowerCamelCase__ : Dict = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
lowerCamelCase__ : str = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowerCamelCase__ : Any = False
@property
def a__ (self ):
'''simple docstring'''
return 3_2
@property
def a__ (self ):
'''simple docstring'''
return 3_2
@property
def a__ (self ):
'''simple docstring'''
return self.time_input_dim
@property
def a__ (self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def a__ (self ):
'''simple docstring'''
return 1_0_0
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Optional[int] = {
'in_channels': 8,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image_hint',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
lowerCamelCase__ : int = UNetaDConditionModel(**lowerCamelCase_ )
return model
@property
def a__ (self ):
'''simple docstring'''
return {
"block_out_channels": [3_2, 3_2, 6_4, 6_4],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.dummy_unet
lowerCamelCase__ : List[Any] = self.dummy_movq
lowerCamelCase__ : Tuple = {
'num_train_timesteps': 1_0_0_0,
'beta_schedule': 'linear',
'beta_start': 0.00_085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
lowerCamelCase__ : Optional[Any] = DDIMScheduler(**lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def a__ (self, lowerCamelCase_, lowerCamelCase_=0 ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
lowerCamelCase__ : int = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1 ) ).to(
lowerCamelCase_ )
# create init_image
lowerCamelCase__ : Any = floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
lowerCamelCase__ : Dict = image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase__ : Optional[Any] = Image.fromarray(np.uinta(lowerCamelCase_ ) ).convert('RGB' ).resize((2_5_6, 2_5_6) )
# create hint
lowerCamelCase__ : Dict = floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
if str(lowerCamelCase_ ).startswith('mps' ):
lowerCamelCase__ : int = torch.manual_seed(lowerCamelCase_ )
else:
lowerCamelCase__ : Any = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = {
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'hint': hint,
'generator': generator,
'height': 6_4,
'width': 6_4,
'num_inference_steps': 1_0,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = 'cpu'
lowerCamelCase__ : List[Any] = self.get_dummy_components()
lowerCamelCase__ : List[Any] = self.pipeline_class(**lowerCamelCase_ )
lowerCamelCase__ : Dict = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : Any = pipe(**self.get_dummy_inputs(lowerCamelCase_ ) )
lowerCamelCase__ : List[Any] = output.images
lowerCamelCase__ : str = pipe(
**self.get_dummy_inputs(lowerCamelCase_ ), return_dict=lowerCamelCase_, )[0]
lowerCamelCase__ : int = image[0, -3:, -3:, -1]
lowerCamelCase__ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowerCamelCase__ : List[str] = np.array(
[0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy' )
lowerCamelCase__ : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
lowerCamelCase__ : Any = init_image.resize((5_1_2, 5_1_2) )
lowerCamelCase__ : List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/hint_image_cat.png' )
lowerCamelCase__ : Any = torch.from_numpy(np.array(lowerCamelCase_ ) ).float() / 255.0
lowerCamelCase__ : Optional[int] = hint.permute(2, 0, 1 ).unsqueeze(0 )
lowerCamelCase__ : Union[str, Any] = 'A robot, 4k photo'
lowerCamelCase__ : Any = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior', torch_dtype=torch.floataa )
pipe_prior.to(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-controlnet-depth', torch_dtype=torch.floataa )
lowerCamelCase__ : int = pipeline.to(lowerCamelCase_ )
pipeline.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : str = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = pipe_prior(
lowerCamelCase_, image=lowerCamelCase_, strength=0.85, generator=lowerCamelCase_, negative_prompt='', ).to_tuple()
lowerCamelCase__ : Union[str, Any] = pipeline(
image=lowerCamelCase_, image_embeds=lowerCamelCase_, negative_image_embeds=lowerCamelCase_, hint=lowerCamelCase_, generator=lowerCamelCase_, num_inference_steps=1_0_0, height=5_1_2, width=5_1_2, strength=0.5, output_type='np', )
lowerCamelCase__ : Dict = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[str] = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = 'canine'
def __init__(self, lowerCamelCase_=7_6_8, lowerCamelCase_=1_2, lowerCamelCase_=1_2, lowerCamelCase_=3_0_7_2, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=1_6_3_8_4, lowerCamelCase_=1_6, lowerCamelCase_=0.02, lowerCamelCase_=1e-12, lowerCamelCase_=0, lowerCamelCase_=0xE000, lowerCamelCase_=0xE001, lowerCamelCase_=4, lowerCamelCase_=4, lowerCamelCase_=8, lowerCamelCase_=1_6_3_8_4, lowerCamelCase_=1_2_8, **lowerCamelCase_, ):
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase_, bos_token_id=lowerCamelCase_, eos_token_id=lowerCamelCase_, **lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = max_position_embeddings
lowerCamelCase__ : str = hidden_size
lowerCamelCase__ : str = num_hidden_layers
lowerCamelCase__ : Optional[int] = num_attention_heads
lowerCamelCase__ : str = intermediate_size
lowerCamelCase__ : str = hidden_act
lowerCamelCase__ : Dict = hidden_dropout_prob
lowerCamelCase__ : int = attention_probs_dropout_prob
lowerCamelCase__ : Any = initializer_range
lowerCamelCase__ : Tuple = type_vocab_size
lowerCamelCase__ : Dict = layer_norm_eps
# Character config:
lowerCamelCase__ : Any = downsampling_rate
lowerCamelCase__ : Optional[int] = upsampling_kernel_size
lowerCamelCase__ : Union[str, Any] = num_hash_functions
lowerCamelCase__ : str = num_hash_buckets
lowerCamelCase__ : List[str] = local_transformer_stride
| 696 |
"""simple docstring"""
A_ : List[str] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dataclasses": "dataclasses",
"datasets": "datasets!=2.5.0",
"decord": "decord==0.6.0",
"deepspeed": "deepspeed>=0.9.3",
"diffusers": "diffusers",
"dill": "dill<0.3.5",
"evaluate": "evaluate>=0.2.0",
"fairscale": "fairscale>0.3",
"faiss-cpu": "faiss-cpu",
"fastapi": "fastapi",
"filelock": "filelock",
"flax": "flax>=0.4.1,<=0.7.0",
"ftfy": "ftfy",
"fugashi": "fugashi>=1.0",
"GitPython": "GitPython<3.1.19",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
"importlib_metadata": "importlib_metadata",
"ipadic": "ipadic>=1.0.0,<2.0",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
"jaxlib": "jaxlib>=0.1.65,<=0.4.13",
"jieba": "jieba",
"kenlm": "kenlm",
"keras-nlp": "keras-nlp>=0.3.1",
"librosa": "librosa",
"nltk": "nltk",
"natten": "natten>=0.14.6",
"numpy": "numpy>=1.17",
"onnxconverter-common": "onnxconverter-common",
"onnxruntime-tools": "onnxruntime-tools>=1.4.2",
"onnxruntime": "onnxruntime>=1.4.0",
"opencv-python": "opencv-python",
"optuna": "optuna",
"optax": "optax>=0.0.8,<=0.1.4",
"packaging": "packaging>=20.0",
"parameterized": "parameterized",
"phonemizer": "phonemizer",
"protobuf": "protobuf",
"psutil": "psutil",
"pyyaml": "pyyaml>=5.1",
"pydantic": "pydantic<2",
"pytest": "pytest>=7.2.0",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"python": "python>=3.8.0",
"ray[tune]": "ray[tune]",
"regex": "regex!=2019.12.17",
"requests": "requests",
"rhoknp": "rhoknp>=1.1.0,<1.3.1",
"rjieba": "rjieba",
"rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
"ruff": "ruff>=0.0.241,<=0.0.259",
"sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
"sacremoses": "sacremoses",
"safetensors": "safetensors>=0.3.1",
"sagemaker": "sagemaker>=2.31.0",
"scikit-learn": "scikit-learn",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"sigopt": "sigopt",
"starlette": "starlette",
"sudachipy": "sudachipy>=0.6.6",
"sudachidict_core": "sudachidict_core>=20220729",
"tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
"tensorflow": "tensorflow>=2.6,<2.14",
"tensorflow-text": "tensorflow-text<2.14",
"tf2onnx": "tf2onnx",
"timeout-decorator": "timeout-decorator",
"timm": "timm",
"tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
"torch": "torch>=1.9,!=1.12.0",
"torchaudio": "torchaudio",
"torchvision": "torchvision",
"pyctcdecode": "pyctcdecode>=0.4.0",
"tqdm": "tqdm>=4.27",
"unidic": "unidic>=1.0.2",
"unidic_lite": "unidic_lite>=1.0.7",
"urllib3": "urllib3<2.0.0",
"uvicorn": "uvicorn",
}
| 696 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : torch.FloatTensor
class a_ ( snake_case_ , snake_case_ ):
'''simple docstring'''
@register_to_config
def __init__(self, lowerCamelCase_ = 6_5_5_3_6, lowerCamelCase_ = None, lowerCamelCase_ = 2, lowerCamelCase_ = 2, lowerCamelCase_ = 0, lowerCamelCase_ = "fourier", lowerCamelCase_ = True, lowerCamelCase_ = False, lowerCamelCase_ = 0.0, lowerCamelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"), lowerCamelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"), lowerCamelCase_ = "UNetMidBlock1D", lowerCamelCase_ = None, lowerCamelCase_ = (3_2, 3_2, 6_4), lowerCamelCase_ = None, lowerCamelCase_ = 8, lowerCamelCase_ = 1, lowerCamelCase_ = False, ):
'''simple docstring'''
super().__init__()
lowerCamelCase__ : str = sample_size
# time
if time_embedding_type == "fourier":
lowerCamelCase__ : Dict = GaussianFourierProjection(
embedding_size=8, set_W_to_weight=lowerCamelCase_, log=lowerCamelCase_, flip_sin_to_cos=lowerCamelCase_ )
lowerCamelCase__ : Tuple = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
lowerCamelCase__ : Dict = Timesteps(
block_out_channels[0], flip_sin_to_cos=lowerCamelCase_, downscale_freq_shift=lowerCamelCase_ )
lowerCamelCase__ : str = block_out_channels[0]
if use_timestep_embedding:
lowerCamelCase__ : Tuple = block_out_channels[0] * 4
lowerCamelCase__ : str = TimestepEmbedding(
in_channels=lowerCamelCase_, time_embed_dim=lowerCamelCase_, act_fn=lowerCamelCase_, out_dim=block_out_channels[0], )
lowerCamelCase__ : Optional[int] = nn.ModuleList([] )
lowerCamelCase__ : Any = None
lowerCamelCase__ : Optional[int] = nn.ModuleList([] )
lowerCamelCase__ : int = None
# down
lowerCamelCase__ : Any = in_channels
for i, down_block_type in enumerate(lowerCamelCase_ ):
lowerCamelCase__ : str = output_channel
lowerCamelCase__ : List[str] = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
lowerCamelCase__ : Any = i == len(lowerCamelCase_ ) - 1
lowerCamelCase__ : List[str] = get_down_block(
lowerCamelCase_, num_layers=lowerCamelCase_, in_channels=lowerCamelCase_, out_channels=lowerCamelCase_, temb_channels=block_out_channels[0], add_downsample=not is_final_block or downsample_each_block, )
self.down_blocks.append(lowerCamelCase_ )
# mid
lowerCamelCase__ : Union[str, Any] = get_mid_block(
lowerCamelCase_, in_channels=block_out_channels[-1], mid_channels=block_out_channels[-1], out_channels=block_out_channels[-1], embed_dim=block_out_channels[0], num_layers=lowerCamelCase_, add_downsample=lowerCamelCase_, )
# up
lowerCamelCase__ : List[str] = list(reversed(lowerCamelCase_ ) )
lowerCamelCase__ : Tuple = reversed_block_out_channels[0]
if out_block_type is None:
lowerCamelCase__ : List[str] = out_channels
else:
lowerCamelCase__ : str = block_out_channels[0]
for i, up_block_type in enumerate(lowerCamelCase_ ):
lowerCamelCase__ : Optional[int] = output_channel
lowerCamelCase__ : Optional[Any] = (
reversed_block_out_channels[i + 1] if i < len(lowerCamelCase_ ) - 1 else final_upsample_channels
)
lowerCamelCase__ : Optional[int] = i == len(lowerCamelCase_ ) - 1
lowerCamelCase__ : Tuple = get_up_block(
lowerCamelCase_, num_layers=lowerCamelCase_, in_channels=lowerCamelCase_, out_channels=lowerCamelCase_, temb_channels=block_out_channels[0], add_upsample=not is_final_block, )
self.up_blocks.append(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = output_channel
# out
lowerCamelCase__ : Dict = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 3_2 )
lowerCamelCase__ : Optional[Any] = get_out_block(
out_block_type=lowerCamelCase_, num_groups_out=lowerCamelCase_, embed_dim=block_out_channels[0], out_channels=lowerCamelCase_, act_fn=lowerCamelCase_, fc_dim=block_out_channels[-1] // 4, )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ = True, ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = timestep
if not torch.is_tensor(lowerCamelCase_ ):
lowerCamelCase__ : List[Any] = torch.tensor([timesteps], dtype=torch.long, device=sample.device )
elif torch.is_tensor(lowerCamelCase_ ) and len(timesteps.shape ) == 0:
lowerCamelCase__ : List[str] = timesteps[None].to(sample.device )
lowerCamelCase__ : List[str] = self.time_proj(lowerCamelCase_ )
if self.config.use_timestep_embedding:
lowerCamelCase__ : Union[str, Any] = self.time_mlp(lowerCamelCase_ )
else:
lowerCamelCase__ : Tuple = timestep_embed[..., None]
lowerCamelCase__ : List[str] = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
lowerCamelCase__ : Any = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
lowerCamelCase__ : List[str] = ()
for downsample_block in self.down_blocks:
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = downsample_block(hidden_states=lowerCamelCase_, temb=lowerCamelCase_ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
lowerCamelCase__ : Optional[Any] = self.mid_block(lowerCamelCase_, lowerCamelCase_ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
lowerCamelCase__ : int = down_block_res_samples[-1:]
lowerCamelCase__ : str = down_block_res_samples[:-1]
lowerCamelCase__ : Optional[Any] = upsample_block(lowerCamelCase_, res_hidden_states_tuple=lowerCamelCase_, temb=lowerCamelCase_ )
# 5. post-process
if self.out_block:
lowerCamelCase__ : List[Any] = self.out_block(lowerCamelCase_, lowerCamelCase_ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=lowerCamelCase_ )
| 696 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
A_ : Optional[int] = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_ = 1_4 ):
'''simple docstring'''
if group not in primes:
raise ValueError('Unsupported Group' )
lowerCamelCase__ : int = primes[group]['prime']
lowerCamelCase__ : Optional[int] = primes[group]['generator']
lowerCamelCase__ : Any = int(hexlify(urandom(3_2 ) ), base=1_6 )
def a__ (self ):
'''simple docstring'''
return hex(self.__private_key )[2:]
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = pow(self.generator, self.__private_key, self.prime )
return hex(lowerCamelCase_ )[2:]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
return (
2 <= key <= self.prime - 2
and pow(lowerCamelCase_, (self.prime - 1) // 2, self.prime ) == 1
)
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = int(lowerCamelCase_, base=1_6 )
if not self.is_valid_public_key(lowerCamelCase_ ):
raise ValueError('Invalid public key' )
lowerCamelCase__ : Tuple = pow(lowerCamelCase_, self.__private_key, self.prime )
return shaaaa(str(lowerCamelCase_ ).encode() ).hexdigest()
@staticmethod
def a__ (lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return (
2 <= remote_public_key_str <= prime - 2
and pow(lowerCamelCase_, (prime - 1) // 2, lowerCamelCase_ ) == 1
)
@staticmethod
def a__ (lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ = 1_4 ):
'''simple docstring'''
lowerCamelCase__ : Dict = int(lowerCamelCase_, base=1_6 )
lowerCamelCase__ : List[Any] = int(lowerCamelCase_, base=1_6 )
lowerCamelCase__ : List[str] = primes[group]['prime']
if not DiffieHellman.is_valid_public_key_static(lowerCamelCase_, lowerCamelCase_ ):
raise ValueError('Invalid public key' )
lowerCamelCase__ : Dict = pow(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
return shaaaa(str(lowerCamelCase_ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : Dict = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = [
"GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"GraphormerForGraphClassification",
"GraphormerModel",
"GraphormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
A_ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_lowerCamelCase ) * abs(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 696 | 1 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=1_0_2_4, lowerCamelCase_=1_0_2_4, lowerCamelCase_=3.6 ):
'''simple docstring'''
lowerCamelCase__ : str = tokenizer
lowerCamelCase__ : Any = tokenizer.bos_token_id
lowerCamelCase__ : Dict = dataset
lowerCamelCase__ : Dict = seq_length
lowerCamelCase__ : int = seq_length * chars_per_token * num_of_sequences
def __iter__(self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = iter(self.dataset )
lowerCamelCase__ : int = True
while more_examples:
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(lowerCamelCase_ )['content'] )
buffer_len += len(buffer[-1] )
except StopIteration:
lowerCamelCase__ : Union[str, Any] = False
break
lowerCamelCase__ : Union[str, Any] = tokenizer(lowerCamelCase_, truncation=lowerCamelCase_ )['input_ids']
lowerCamelCase__ : Dict = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0, len(lowerCamelCase_ ), self.seq_length ):
lowerCamelCase__ : List[Any] = all_token_ids[i : i + self.seq_length]
if len(lowerCamelCase_ ) == self.seq_length:
yield torch.tensor(lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Tuple = {'streaming': True}
lowerCamelCase__ : Optional[Any] = load_dataset(args.dataset_name , split='train' , **_lowerCamelCase )
lowerCamelCase__ : int = ConstantLengthDataset(_lowerCamelCase , _lowerCamelCase , seq_length=args.seq_length )
lowerCamelCase__ : List[Any] = DataLoader(_lowerCamelCase , batch_size=args.batch_size )
return eval_dataloader
def lowerCamelCase_ ( _lowerCamelCase ):
model.eval()
lowerCamelCase__ : int = []
for step, batch in enumerate(_lowerCamelCase ):
with torch.no_grad():
lowerCamelCase__ : int = model(_lowerCamelCase , labels=_lowerCamelCase )
lowerCamelCase__ : List[Any] = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_lowerCamelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
lowerCamelCase__ : Any = torch.mean(torch.cat(_lowerCamelCase ) )
try:
lowerCamelCase__ : Dict = torch.exp(_lowerCamelCase )
except OverflowError:
lowerCamelCase__ : Optional[Any] = float('inf' )
return loss.item(), perplexity.item()
# Setup Accelerator
A_ : Optional[int] = Accelerator()
# Parse configuration
A_ : Any = HfArgumentParser(EvaluationArguments)
A_ : Dict = parser.parse_args()
set_seed(args.seed)
# Logging
A_ : Optional[int] = logging.getLogger(__name__)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
# Load model and tokenizer
A_ : str = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A_ : int = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
A_ : List[Any] = create_dataloader(args)
# Prepare everything with our `accelerator`.
A_, A_ : Any = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
A_, A_ : Optional[Any] = evaluate(args)
logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")
| 696 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
A_ : int = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 1_28,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
@is_staging_test
class a_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def a__ (cls ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def a__ (cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='test-dynamic-config' )
except HTTPError:
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('test-config', use_auth_token=self._token )
lowerCamelCase__ : Optional[int] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase_, repo_id='test-config', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : List[str] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org', use_auth_token=self._token )
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase_, repo_id='valid_org/test-config-org', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : str = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
CustomConfig.register_for_auto_class()
lowerCamelCase__ : Optional[int] = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config', use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map, {'AutoConfig': 'custom_configuration.CustomConfig'} )
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config''', trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__, 'CustomConfig' )
self.assertEqual(new_config.attribute, 4_2 )
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
lowerCamelCase__ : Tuple = c.n_embd + 1 # int
lowerCamelCase__ : Union[str, Any] = c.resid_pdrop + 1.0 # float
lowerCamelCase__ : List[Any] = not c.scale_attn_weights # bool
lowerCamelCase__ : List[Any] = c.summary_type + 'foo' # str
c.update_from_string(
f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(lowerCamelCase_, c.n_embd, 'mismatch for key: n_embd' )
self.assertEqual(lowerCamelCase_, c.resid_pdrop, 'mismatch for key: resid_pdrop' )
self.assertEqual(lowerCamelCase_, c.scale_attn_weights, 'mismatch for key: scale_attn_weights' )
self.assertEqual(lowerCamelCase_, c.summary_type, 'mismatch for key: summary_type' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = PretrainedConfig()
lowerCamelCase__ : Optional[Any] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowerCamelCase_, ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
lowerCamelCase__ : Any = [key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase_, lowerCamelCase_ )]
if len(lowerCamelCase_ ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
f''' {', '.join(lowerCamelCase_ )}.''' )
def a__ (self ):
'''simple docstring'''
with self.assertRaises(lowerCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
lowerCamelCase__ : int = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder', subfolder='bert' )
self.assertIsNotNone(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = mock.Mock()
lowerCamelCase__ : List[str] = 5_0_0
lowerCamelCase__ : Any = {}
lowerCamelCase__ : int = HTTPError
lowerCamelCase__ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
lowerCamelCase__ : Any = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request', return_value=lowerCamelCase_ ) as mock_head:
lowerCamelCase__ : List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = AutoConfig.from_pretrained('bert-base-cased' )
lowerCamelCase__ : str = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = 2
json.dump(configuration.to_dict(), open(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
lowerCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
lowerCamelCase__ : str = ['config.42.0.0.json']
lowerCamelCase__ : Union[str, Any] = 7_6_8
configuration.save_pretrained(lowerCamelCase_ )
shutil.move(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), os.path.join(lowerCamelCase_, 'config.42.0.0.json' ) )
lowerCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 7_6_8 )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
lowerCamelCase__ : Optional[int] = 'v4.0.0'
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
lowerCamelCase_, return_unused_kwargs=lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowerCamelCase_, {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
lowerCamelCase__ : Dict = 'v3.0.0'
lowerCamelCase__ : List[str] = old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(old_configuration.hidden_size, 7_6_8 )
| 696 | 1 |
"""simple docstring"""
from __future__ import annotations
A_ : int = "#"
class a_ :
'''simple docstring'''
def __init__(self ):
'''simple docstring'''
lowerCamelCase__ : dict = {}
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : int = self._trie
for char in text:
if char not in trie:
lowerCamelCase__ : Dict = {}
lowerCamelCase__ : Optional[Any] = trie[char]
lowerCamelCase__ : List[str] = True
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[str] = self._trie
for char in prefix:
if char in trie:
lowerCamelCase__ : Optional[Any] = trie[char]
else:
return []
return self._elements(lowerCamelCase_ )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = []
for c, v in d.items():
lowerCamelCase__ : List[str] = [' '] if c == END else [(c + s) for s in self._elements(lowerCamelCase_ )]
result.extend(lowerCamelCase_ )
return tuple(lowerCamelCase_ )
A_ : List[Any] = Trie()
A_ : int = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
trie.insert_word(word)
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : str = trie.find_word(_lowerCamelCase )
return tuple(string + word for word in suffixes )
def lowerCamelCase_ ( ):
print(autocomplete_using_trie('de' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 696 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
super().__init__()
lowerCamelCase__ : Dict = value_function
lowerCamelCase__ : int = unet
lowerCamelCase__ : Union[str, Any] = scheduler
lowerCamelCase__ : int = env
lowerCamelCase__ : List[Any] = env.get_dataset()
lowerCamelCase__ : Dict = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
lowerCamelCase__ : Optional[int] = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Tuple = self.data[key].std()
except: # noqa: E722
pass
lowerCamelCase__ : Optional[Any] = env.observation_space.shape[0]
lowerCamelCase__ : List[str] = env.action_space.shape[0]
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
if type(lowerCamelCase_ ) is dict:
return {k: self.to_torch(lowerCamelCase_ ) for k, v in x_in.items()}
elif torch.is_tensor(lowerCamelCase_ ):
return x_in.to(self.unet.device )
return torch.tensor(lowerCamelCase_, device=self.unet.device )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
for key, val in cond.items():
lowerCamelCase__ : Optional[Any] = val.clone()
return x_in
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = x.shape[0]
lowerCamelCase__ : Tuple = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowerCamelCase__ : Dict = torch.full((batch_size,), lowerCamelCase_, device=self.unet.device, dtype=torch.long )
for _ in range(lowerCamelCase_ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowerCamelCase__ : str = self.value_function(x.permute(0, 2, 1 ), lowerCamelCase_ ).sample
lowerCamelCase__ : Union[str, Any] = torch.autograd.grad([y.sum()], [x] )[0]
lowerCamelCase__ : Optional[int] = self.scheduler._get_variance(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = torch.exp(0.5 * posterior_variance )
lowerCamelCase__ : Tuple = model_std * grad
lowerCamelCase__ : str = 0
lowerCamelCase__ : Dict = x.detach()
lowerCamelCase__ : Dict = x + scale * grad
lowerCamelCase__ : Optional[int] = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : Tuple = self.unet(x.permute(0, 2, 1 ), lowerCamelCase_ ).sample.permute(0, 2, 1 )
# TODO: verify deprecation of this kwarg
lowerCamelCase__ : Optional[Any] = self.scheduler.step(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, predict_epsilon=lowerCamelCase_ )['prev_sample']
# apply conditions to the trajectory (set the initial state)
lowerCamelCase__ : Any = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : List[str] = self.to_torch(lowerCamelCase_ )
return x, y
def __call__(self, lowerCamelCase_, lowerCamelCase_=6_4, lowerCamelCase_=3_2, lowerCamelCase_=2, lowerCamelCase_=0.1 ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.normalize(lowerCamelCase_, 'observations' )
lowerCamelCase__ : List[str] = obs[None].repeat(lowerCamelCase_, axis=0 )
lowerCamelCase__ : str = {0: self.to_torch(lowerCamelCase_ )}
lowerCamelCase__ : Optional[Any] = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowerCamelCase__ : List[Any] = randn_tensor(lowerCamelCase_, device=self.unet.device )
lowerCamelCase__ : int = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : List[str] = self.to_torch(lowerCamelCase_ )
# run the diffusion process
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.run_diffusion(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
# sort output trajectories by value
lowerCamelCase__ : Union[str, Any] = y.argsort(0, descending=lowerCamelCase_ ).squeeze()
lowerCamelCase__ : List[str] = x[sorted_idx]
lowerCamelCase__ : Optional[Any] = sorted_values[:, :, : self.action_dim]
lowerCamelCase__ : Union[str, Any] = actions.detach().cpu().numpy()
lowerCamelCase__ : Union[str, Any] = self.de_normalize(lowerCamelCase_, key='actions' )
# select the action with the highest value
if y is not None:
lowerCamelCase__ : str = 0
else:
# if we didn't run value guiding, select a random action
lowerCamelCase__ : Optional[Any] = np.random.randint(0, lowerCamelCase_ )
lowerCamelCase__ : Tuple = denorm_actions[selected_index, 0]
return denorm_actions
| 696 | 1 |
"""simple docstring"""
from ....utils import logging
A_ : List[str] = logging.get_logger(__name__)
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=2_0_4_8 ):
'''simple docstring'''
lowerCamelCase__ : Tuple = config.__dict__
lowerCamelCase__ : List[str] = modal_hidden_size
if num_labels:
lowerCamelCase__ : List[str] = num_labels
| 696 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ , lowerCamelCase__ : List[str] = analyze_text(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = list(' ' + ascii_lowercase )
# what is our total sum of probabilities.
lowerCamelCase__ : List[Any] = sum(single_char_strings.values() )
# one length string
lowerCamelCase__ : str = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
lowerCamelCase__ : Tuple = single_char_strings[ch]
lowerCamelCase__ : Union[str, Any] = my_str / all_sum
my_fir_sum += prob * math.loga(_lowerCamelCase ) # entropy formula.
# print entropy
print(f'''{round(-1 * my_fir_sum ):.1f}''' )
# two len string
lowerCamelCase__ : Dict = sum(two_char_strings.values() )
lowerCamelCase__ : str = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
lowerCamelCase__ : int = cha + cha
if sequence in two_char_strings:
lowerCamelCase__ : int = two_char_strings[sequence]
lowerCamelCase__ : Tuple = int(_lowerCamelCase ) / all_sum
my_sec_sum += prob * math.loga(_lowerCamelCase )
# print second entropy
print(f'''{round(-1 * my_sec_sum ):.1f}''' )
# print the difference between them
print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}''' )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[str] = Counter() # type: ignore
lowerCamelCase__ : List[Any] = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(_lowerCamelCase ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def lowerCamelCase_ ( ):
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 696 | 1 |
"""simple docstring"""
A_ : List[str] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dataclasses": "dataclasses",
"datasets": "datasets!=2.5.0",
"decord": "decord==0.6.0",
"deepspeed": "deepspeed>=0.9.3",
"diffusers": "diffusers",
"dill": "dill<0.3.5",
"evaluate": "evaluate>=0.2.0",
"fairscale": "fairscale>0.3",
"faiss-cpu": "faiss-cpu",
"fastapi": "fastapi",
"filelock": "filelock",
"flax": "flax>=0.4.1,<=0.7.0",
"ftfy": "ftfy",
"fugashi": "fugashi>=1.0",
"GitPython": "GitPython<3.1.19",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
"importlib_metadata": "importlib_metadata",
"ipadic": "ipadic>=1.0.0,<2.0",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
"jaxlib": "jaxlib>=0.1.65,<=0.4.13",
"jieba": "jieba",
"kenlm": "kenlm",
"keras-nlp": "keras-nlp>=0.3.1",
"librosa": "librosa",
"nltk": "nltk",
"natten": "natten>=0.14.6",
"numpy": "numpy>=1.17",
"onnxconverter-common": "onnxconverter-common",
"onnxruntime-tools": "onnxruntime-tools>=1.4.2",
"onnxruntime": "onnxruntime>=1.4.0",
"opencv-python": "opencv-python",
"optuna": "optuna",
"optax": "optax>=0.0.8,<=0.1.4",
"packaging": "packaging>=20.0",
"parameterized": "parameterized",
"phonemizer": "phonemizer",
"protobuf": "protobuf",
"psutil": "psutil",
"pyyaml": "pyyaml>=5.1",
"pydantic": "pydantic<2",
"pytest": "pytest>=7.2.0",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"python": "python>=3.8.0",
"ray[tune]": "ray[tune]",
"regex": "regex!=2019.12.17",
"requests": "requests",
"rhoknp": "rhoknp>=1.1.0,<1.3.1",
"rjieba": "rjieba",
"rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
"ruff": "ruff>=0.0.241,<=0.0.259",
"sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
"sacremoses": "sacremoses",
"safetensors": "safetensors>=0.3.1",
"sagemaker": "sagemaker>=2.31.0",
"scikit-learn": "scikit-learn",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"sigopt": "sigopt",
"starlette": "starlette",
"sudachipy": "sudachipy>=0.6.6",
"sudachidict_core": "sudachidict_core>=20220729",
"tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
"tensorflow": "tensorflow>=2.6,<2.14",
"tensorflow-text": "tensorflow-text<2.14",
"tf2onnx": "tf2onnx",
"timeout-decorator": "timeout-decorator",
"timm": "timm",
"tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
"torch": "torch>=1.9,!=1.12.0",
"torchaudio": "torchaudio",
"torchvision": "torchvision",
"pyctcdecode": "pyctcdecode>=0.4.0",
"tqdm": "tqdm>=4.27",
"unidic": "unidic>=1.0.2",
"unidic_lite": "unidic_lite>=1.0.7",
"urllib3": "urllib3<2.0.0",
"uvicorn": "uvicorn",
}
| 696 |
"""simple docstring"""
import os
def lowerCamelCase_ ( ):
with open(os.path.dirname(_lowerCamelCase ) + '/p022_names.txt' ) as file:
lowerCamelCase__ : Union[str, Any] = str(file.readlines()[0] )
lowerCamelCase__ : int = names.replace('"' , '' ).split(',' )
names.sort()
lowerCamelCase__ : Tuple = 0
lowerCamelCase__ : str = 0
for i, name in enumerate(_lowerCamelCase ):
for letter in name:
name_score += ord(_lowerCamelCase ) - 64
total_score += (i + 1) * name_score
lowerCamelCase__ : Dict = 0
return total_score
if __name__ == "__main__":
print(solution())
| 696 | 1 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
A_ : List[str] = getLogger(__name__)
A_ : str = "cuda" if torch.cuda.is_available() else "cpu"
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 8 , _lowerCamelCase = DEFAULT_DEVICE , _lowerCamelCase=False , _lowerCamelCase="summarization" , _lowerCamelCase=None , **_lowerCamelCase , ):
lowerCamelCase__ : str = Path(_lowerCamelCase ).open('w' , encoding='utf-8' )
lowerCamelCase__ : Any = str(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase ).to(_lowerCamelCase )
if fpaa:
lowerCamelCase__ : Optional[Any] = model.half()
lowerCamelCase__ : str = AutoTokenizer.from_pretrained(_lowerCamelCase )
logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}''' ) # if this is wrong, check config.model_type.
lowerCamelCase__ : List[Any] = time.time()
# update config with task specific params
use_task_specific_params(_lowerCamelCase , _lowerCamelCase )
if prefix is None:
lowerCamelCase__ : Any = prefix or getattr(model.config , 'prefix' , '' ) or ''
for examples_chunk in tqdm(list(chunks(_lowerCamelCase , _lowerCamelCase ) ) ):
lowerCamelCase__ : Optional[int] = [prefix + text for text in examples_chunk]
lowerCamelCase__ : str = tokenizer(_lowerCamelCase , return_tensors='pt' , truncation=_lowerCamelCase , padding='longest' ).to(_lowerCamelCase )
lowerCamelCase__ : Dict = model.generate(
input_ids=batch.input_ids , attention_mask=batch.attention_mask , **_lowerCamelCase , )
lowerCamelCase__ : str = tokenizer.batch_decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
for hypothesis in dec:
fout.write(hypothesis + '\n' )
fout.flush()
fout.close()
lowerCamelCase__ : Union[str, Any] = int(time.time() - start_time ) # seconds
lowerCamelCase__ : Any = len(_lowerCamelCase )
return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )}
def lowerCamelCase_ ( ):
return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )
def lowerCamelCase_ ( _lowerCamelCase=True ):
lowerCamelCase__ : List[str] = argparse.ArgumentParser()
parser.add_argument('model_name' , type=_lowerCamelCase , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('input_path' , type=_lowerCamelCase , help='like cnn_dm/test.source' )
parser.add_argument('save_path' , type=_lowerCamelCase , help='where to save summaries' )
parser.add_argument('--reference_path' , type=_lowerCamelCase , required=_lowerCamelCase , help='like cnn_dm/test.target' )
parser.add_argument('--score_path' , type=_lowerCamelCase , required=_lowerCamelCase , default='metrics.json' , help='where to save metrics' )
parser.add_argument('--device' , type=_lowerCamelCase , required=_lowerCamelCase , default=_lowerCamelCase , help='cuda, cuda:1, cpu etc.' )
parser.add_argument(
'--prefix' , type=_lowerCamelCase , required=_lowerCamelCase , default=_lowerCamelCase , help='will be added to the begininng of src examples' )
parser.add_argument('--task' , type=_lowerCamelCase , default='summarization' , help='used for task_specific_params + metrics' )
parser.add_argument('--bs' , type=_lowerCamelCase , default=8 , required=_lowerCamelCase , help='batch size' )
parser.add_argument(
'--n_obs' , type=_lowerCamelCase , default=-1 , required=_lowerCamelCase , help='How many observations. Defaults to all.' )
parser.add_argument('--fp16' , action='store_true' )
parser.add_argument('--dump-args' , action='store_true' , help='print the custom hparams with the results' )
parser.add_argument(
'--info' , nargs='?' , type=_lowerCamelCase , const=datetime_now() , help=(
'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
' lang=en-ru. If no value is passed, the current datetime string will be used.'
) , )
# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
lowerCamelCase__ , lowerCamelCase__ : Tuple = parser.parse_known_args()
lowerCamelCase__ : Tuple = parse_numeric_n_bool_cl_kwargs(_lowerCamelCase )
if parsed_args and verbose:
print(f'''parsed the following generate kwargs: {parsed_args}''' )
lowerCamelCase__ : Any = [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()]
if args.n_obs > 0:
lowerCamelCase__ : Optional[int] = examples[: args.n_obs]
Path(args.save_path ).parent.mkdir(exist_ok=_lowerCamelCase )
if args.reference_path is None and Path(args.score_path ).exists():
warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.''' )
if args.device == "cpu" and args.fpaa:
# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
raise ValueError('Can\'t mix --fp16 and --device cpu' )
lowerCamelCase__ : Any = generate_summaries_or_translations(
_lowerCamelCase , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **_lowerCamelCase , )
if args.reference_path is None:
return {}
# Compute scores
lowerCamelCase__ : List[str] = calculate_bleu if 'translation' in args.task else calculate_rouge
lowerCamelCase__ : Optional[Any] = [x.rstrip() for x in open(args.save_path ).readlines()]
lowerCamelCase__ : int = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_lowerCamelCase )]
lowerCamelCase__ : dict = score_fn(_lowerCamelCase , _lowerCamelCase )
scores.update(_lowerCamelCase )
if args.dump_args:
scores.update(_lowerCamelCase )
if args.info:
lowerCamelCase__ : List[str] = args.info
if verbose:
print(_lowerCamelCase )
if args.score_path is not None:
json.dump(_lowerCamelCase , open(args.score_path , 'w' ) )
return scores
if __name__ == "__main__":
# Usage for MT:
# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@
run_generate(verbose=True)
| 696 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : int = 'Speech2TextFeatureExtractor'
lowerCamelCase__ : Dict = 'Speech2TextTokenizer'
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
super().__init__(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : List[str] = self.feature_extractor
lowerCamelCase__ : List[Any] = False
def __call__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*lowerCamelCase_, **lowerCamelCase_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
lowerCamelCase__ : Optional[int] = kwargs.pop('raw_speech' )
else:
lowerCamelCase__ : int = kwargs.pop('audio', lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = kwargs.pop('sampling_rate', lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = kwargs.pop('text', lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
lowerCamelCase__ : List[str] = args[0]
lowerCamelCase__ : Any = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
lowerCamelCase__ : Union[str, Any] = self.feature_extractor(lowerCamelCase_, *lowerCamelCase_, sampling_rate=lowerCamelCase_, **lowerCamelCase_ )
if text is not None:
lowerCamelCase__ : List[Any] = self.tokenizer(lowerCamelCase_, **lowerCamelCase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCamelCase__ : Tuple = encodings['input_ids']
return inputs
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase_, **lowerCamelCase_ )
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase_, **lowerCamelCase_ )
@contextmanager
def a__ (self ):
'''simple docstring'''
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
lowerCamelCase__ : int = True
lowerCamelCase__ : List[Any] = self.tokenizer
yield
lowerCamelCase__ : Optional[int] = self.feature_extractor
lowerCamelCase__ : List[Any] = False
| 696 | 1 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = sorted(zip(_lowerCamelCase , _lowerCamelCase ) , key=lambda _lowerCamelCase : x[0] / x[1] , reverse=_lowerCamelCase )
lowerCamelCase__ , lowerCamelCase__ : str = [i[0] for i in r], [i[1] for i in r]
lowerCamelCase__ : Optional[Any] = list(accumulate(_lowerCamelCase ) )
lowerCamelCase__ : int = bisect(_lowerCamelCase , _lowerCamelCase )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=9_9, lowerCamelCase_=6_4, lowerCamelCase_=3_2, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = parent
lowerCamelCase__ : Union[str, Any] = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : List[str] = is_training
lowerCamelCase__ : Optional[Any] = use_input_mask
lowerCamelCase__ : List[Any] = use_token_type_ids
lowerCamelCase__ : List[Any] = use_labels
lowerCamelCase__ : Optional[Any] = vocab_size
lowerCamelCase__ : str = hidden_size
lowerCamelCase__ : Optional[int] = embedding_size
lowerCamelCase__ : List[str] = num_hidden_layers
lowerCamelCase__ : Any = num_attention_heads
lowerCamelCase__ : Any = intermediate_size
lowerCamelCase__ : Union[str, Any] = hidden_act
lowerCamelCase__ : str = hidden_dropout_prob
lowerCamelCase__ : Tuple = attention_probs_dropout_prob
lowerCamelCase__ : Any = max_position_embeddings
lowerCamelCase__ : Any = type_vocab_size
lowerCamelCase__ : List[Any] = type_sequence_label_size
lowerCamelCase__ : Dict = initializer_range
lowerCamelCase__ : Optional[Any] = num_labels
lowerCamelCase__ : Dict = num_choices
lowerCamelCase__ : Tuple = scope
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : List[str] = None
if self.use_input_mask:
lowerCamelCase__ : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Any = None
if self.use_token_type_ids:
lowerCamelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : Any = None
lowerCamelCase__ : Union[str, Any] = None
if self.use_labels:
lowerCamelCase__ : int = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : int = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : str = ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ):
'''simple docstring'''
return MobileBertConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, embedding_size=self.embedding_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=lowerCamelCase_, initializer_range=self.initializer_range, )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = MobileBertModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_, token_type_ids=lowerCamelCase_ )
lowerCamelCase__ : Tuple = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = MobileBertForMaskedLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = MobileBertForNextSentencePrediction(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : str = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = MobileBertForPreTraining(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, next_sentence_label=lowerCamelCase_, )
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = MobileBertForQuestionAnswering(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, start_positions=lowerCamelCase_, end_positions=lowerCamelCase_, )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : int = MobileBertForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.num_labels
lowerCamelCase__ : Optional[int] = MobileBertForTokenClassification(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : int = self.num_choices
lowerCamelCase__ : Dict = MobileBertForMultipleChoice(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = input_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : int = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : List[str] = config_and_inputs
lowerCamelCase__ : Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Dict = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : Tuple = (
{
'feature-extraction': MobileBertModel,
'fill-mask': MobileBertForMaskedLM,
'question-answering': MobileBertForQuestionAnswering,
'text-classification': MobileBertForSequenceClassification,
'token-classification': MobileBertForTokenClassification,
'zero-shot': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : int = True
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=False ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = super()._prepare_for_class(lowerCamelCase_, lowerCamelCase_, return_labels=lowerCamelCase_ )
if return_labels:
if model_class in get_values(lowerCamelCase_ ):
lowerCamelCase__ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=lowerCamelCase_ )
return inputs_dict
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = MobileBertModelTester(self )
lowerCamelCase__ : List[str] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=3_7 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
return torch.tensor(
_lowerCamelCase , dtype=torch.long , device=_lowerCamelCase , )
A_ : Tuple = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = MobileBertModel.from_pretrained('google/mobilebert-uncased' ).to(lowerCamelCase_ )
lowerCamelCase__ : Tuple = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] )
with torch.no_grad():
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_ )[0]
lowerCamelCase__ : Optional[int] = torch.Size((1, 9, 5_1_2) )
self.assertEqual(output.shape, lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = torch.tensor(
[
[
[-2.4_736_526e07, 8.2_691_656e04, 1.6_521_838e05],
[-5.7_541_704e-01, 3.9_056_022e00, 4.4_011_507e00],
[2.6_047_359e00, 1.5_677_652e00, -1.7_324_188e-01],
]
], device=lowerCamelCase_, )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
lowerCamelCase__ : Optional[int] = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
lowerCamelCase__ : Any = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 696 | 1 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Tuple = int(np.ceil((x_end - xa) / step_size ) )
lowerCamelCase__ : List[str] = np.zeros((n + 1,) )
lowerCamelCase__ : str = ya
lowerCamelCase__ : Any = xa
for k in range(_lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = y[k] + step_size * ode_func(_lowerCamelCase , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
A_ : str = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=1 ):
'''simple docstring'''
lowerCamelCase__ : Any = tokenizer
lowerCamelCase__ : Optional[Any] = dataset
lowerCamelCase__ : int = len(lowerCamelCase_ ) if n_tasks is None else n_tasks
lowerCamelCase__ : Any = n_copies
def __iter__(self ):
'''simple docstring'''
lowerCamelCase__ : Dict = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
lowerCamelCase__ : Optional[int] = self.tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = start_length
lowerCamelCase__ : List[str] = eof_strings
lowerCamelCase__ : List[str] = tokenizer
def __call__(self, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
lowerCamelCase__ : Optional[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = re.split('(%s)' % '|'.join(_lowerCamelCase ) , _lowerCamelCase )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=20 , **_lowerCamelCase ):
lowerCamelCase__ : List[str] = defaultdict(_lowerCamelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_lowerCamelCase ) ):
with torch.no_grad():
lowerCamelCase__ : str = batch['ids'].shape[-1]
lowerCamelCase__ : int = accelerator.unwrap_model(_lowerCamelCase ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=_lowerCamelCase , **_lowerCamelCase )
# each task is generated batch_size times
lowerCamelCase__ : Optional[Any] = batch['task_id'].repeat(_lowerCamelCase )
lowerCamelCase__ : List[Any] = accelerator.pad_across_processes(
_lowerCamelCase , dim=1 , pad_index=tokenizer.pad_token_id )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
lowerCamelCase__ : List[Any] = generated_tokens.cpu().numpy()
lowerCamelCase__ : Union[str, Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_lowerCamelCase , _lowerCamelCase ):
gen_token_dict[task].append(_lowerCamelCase )
lowerCamelCase__ : str = [[] for _ in range(_lowerCamelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
lowerCamelCase__ : Optional[Any] = tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
code_gens[task].append(remove_last_block(_lowerCamelCase ) )
return code_gens
def lowerCamelCase_ ( ):
# Setup configuration
lowerCamelCase__ : int = HfArgumentParser(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
lowerCamelCase__ : List[str] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
lowerCamelCase__ : Tuple = 'false'
if args.num_workers is None:
lowerCamelCase__ : List[Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
lowerCamelCase__ : List[Any] = Accelerator()
set_seed(args.seed , device_specific=_lowerCamelCase )
# Load model and tokenizer
lowerCamelCase__ : Any = AutoTokenizer.from_pretrained(args.model_ckpt )
lowerCamelCase__ : Optional[int] = tokenizer.eos_token
lowerCamelCase__ : Any = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
lowerCamelCase__ : Optional[Any] = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , _lowerCamelCase , _lowerCamelCase )] ),
}
# Load evaluation dataset and metric
lowerCamelCase__ : Any = load_dataset('openai_humaneval' )
lowerCamelCase__ : Optional[int] = load_metric('code_eval' )
lowerCamelCase__ : List[Any] = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
lowerCamelCase__ : Optional[int] = args.n_samples // args.batch_size
lowerCamelCase__ : Tuple = TokenizedDataset(_lowerCamelCase , human_eval['test'] , n_copies=_lowerCamelCase , n_tasks=_lowerCamelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
lowerCamelCase__ : Union[str, Any] = DataLoader(_lowerCamelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
lowerCamelCase__ : List[Any] = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
lowerCamelCase__ , lowerCamelCase__ : str = accelerator.prepare(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Any = complete_code(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , n_tasks=_lowerCamelCase , batch_size=args.batch_size , **_lowerCamelCase , )
if accelerator.is_main_process:
lowerCamelCase__ : List[str] = []
for task in tqdm(range(_lowerCamelCase ) ):
lowerCamelCase__ : int = human_eval['test'][task]['test']
lowerCamelCase__ : Union[str, Any] = f'''check({human_eval['test'][task]['entry_point']})'''
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
lowerCamelCase__ , lowerCamelCase__ : Any = code_eval_metric.compute(
references=_lowerCamelCase , predictions=_lowerCamelCase , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 696 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Any = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')
lowerCamelCase__ : Optional[int] = (
('layer.', 'layer_'),
('word_embeddings.weight', 'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'),
('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'),
('weight', 'kernel'),
)
if not os.path.isdir(_lowerCamelCase ):
os.makedirs(_lowerCamelCase )
lowerCamelCase__ : List[str] = model.state_dict()
def to_tf_var_name(_lowerCamelCase ):
for patt, repl in iter(_lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = name.replace(_lowerCamelCase , _lowerCamelCase )
return f'''bert/{name}'''
def create_tf_var(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = tf.dtypes.as_dtype(tensor.dtype )
lowerCamelCase__ : Tuple = tf.get_variable(dtype=_lowerCamelCase , shape=tensor.shape , name=_lowerCamelCase , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(_lowerCamelCase )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
lowerCamelCase__ : List[Any] = to_tf_var_name(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
lowerCamelCase__ : Optional[Any] = torch_tensor.T
lowerCamelCase__ : int = create_tf_var(tensor=_lowerCamelCase , name=_lowerCamelCase , session=_lowerCamelCase )
tf.keras.backend.set_value(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : int = session.run(_lowerCamelCase )
print(f'''Successfully created {tf_name}: {np.allclose(_lowerCamelCase , _lowerCamelCase )}''' )
lowerCamelCase__ : Tuple = tf.train.Saver(tf.trainable_variables() )
saver.save(_lowerCamelCase , os.path.join(_lowerCamelCase , model_name.replace('-' , '_' ) + '.ckpt' ) )
def lowerCamelCase_ ( _lowerCamelCase=None ):
lowerCamelCase__ : List[Any] = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=_lowerCamelCase , required=_lowerCamelCase , help='model name e.g. bert-base-uncased' )
parser.add_argument(
'--cache_dir' , type=_lowerCamelCase , default=_lowerCamelCase , required=_lowerCamelCase , help='Directory containing pytorch model' )
parser.add_argument('--pytorch_model_path' , type=_lowerCamelCase , required=_lowerCamelCase , help='/path/to/<pytorch-model-name>.bin' )
parser.add_argument('--tf_cache_dir' , type=_lowerCamelCase , required=_lowerCamelCase , help='Directory in which to save tensorflow model' )
lowerCamelCase__ : List[str] = parser.parse_args(_lowerCamelCase )
lowerCamelCase__ : int = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=_lowerCamelCase , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 696 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : str = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
requires_backends(self, ['speech'] )
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
requires_backends(self, ['speech'] )
| 696 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
A_ : Optional[Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = ["GPTNeoXTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
"GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoXForCausalLM",
"GPTNeoXForQuestionAnswering",
"GPTNeoXForSequenceClassification",
"GPTNeoXForTokenClassification",
"GPTNeoXLayer",
"GPTNeoXModel",
"GPTNeoXPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
A_ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = 0
while number > 0:
lowerCamelCase__ : List[str] = number % 10
sum_of_digits += last_digit
lowerCamelCase__ : str = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def lowerCamelCase_ ( _lowerCamelCase = 100 ):
lowerCamelCase__ : Union[str, Any] = factorial(_lowerCamelCase )
lowerCamelCase__ : List[Any] = split_and_add(_lowerCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 696 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Union[str, Any] = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
"LukeForEntityClassification",
"LukeForEntityPairClassification",
"LukeForEntitySpanClassification",
"LukeForMultipleChoice",
"LukeForQuestionAnswering",
"LukeForSequenceClassification",
"LukeForTokenClassification",
"LukeForMaskedLM",
"LukeModel",
"LukePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A_ : Dict = "pt"
elif is_tf_available():
A_ : Union[str, Any] = "tf"
else:
A_ : List[str] = "jax"
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = PerceiverTokenizer
lowerCamelCase__ : Optional[Any] = False
def a__ (self ):
'''simple docstring'''
super().setUp()
lowerCamelCase__ : int = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def a__ (self ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver' )
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname, **lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_=False, lowerCamelCase_=2_0, lowerCamelCase_=5 ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = []
for i in range(len(lowerCamelCase_ ) ):
try:
lowerCamelCase__ : Any = tokenizer.decode([i], clean_up_tokenization_spaces=lowerCamelCase_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCamelCase__ : Any = list(filter(lambda lowerCamelCase_ : re.match(r'^[ a-zA-Z]+$', t[1] ), lowerCamelCase_ ) )
lowerCamelCase__ : Union[str, Any] = list(filter(lambda lowerCamelCase_ : [t[0]] == tokenizer.encode(t[1], add_special_tokens=lowerCamelCase_ ), lowerCamelCase_ ) )
if max_length is not None and len(lowerCamelCase_ ) > max_length:
lowerCamelCase__ : int = toks[:max_length]
if min_length is not None and len(lowerCamelCase_ ) < min_length and len(lowerCamelCase_ ) > 0:
while len(lowerCamelCase_ ) < min_length:
lowerCamelCase__ : Dict = toks + toks
# toks_str = [t[1] for t in toks]
lowerCamelCase__ : int = [t[0] for t in toks]
# Ensure consistency
lowerCamelCase__ : Optional[int] = tokenizer.decode(lowerCamelCase_, clean_up_tokenization_spaces=lowerCamelCase_ )
if " " not in output_txt and len(lowerCamelCase_ ) > 1:
lowerCamelCase__ : List[Any] = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=lowerCamelCase_ )
+ ' '
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=lowerCamelCase_ )
)
if with_prefix_space:
lowerCamelCase__ : Optional[Any] = ' ' + output_txt
lowerCamelCase__ : List[Any] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
return output_txt, output_ids
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = 'Unicode €.'
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_ )
lowerCamelCase__ : Dict = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : int = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]Unicode €.[SEP]' )
lowerCamelCase__ : List[str] = tokenizer('e è é ê ë' )
lowerCamelCase__ : Dict = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : Any = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]e è é ê ë[SEP]' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ), '[CLS]e è é ê ë[SEP]' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
lowerCamelCase__ : List[Any] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
if FRAMEWORK != "jax":
lowerCamelCase__ : List[str] = list(batch.input_ids.numpy()[0] )
else:
lowerCamelCase__ : int = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertEqual((2, 3_8), batch.input_ids.shape )
self.assertEqual((2, 3_8), batch.attention_mask.shape )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.perceiver_tokenizer
lowerCamelCase__ : List[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCamelCase__ : List[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids', lowerCamelCase_ )
self.assertIn('attention_mask', lowerCamelCase_ )
self.assertNotIn('decoder_input_ids', lowerCamelCase_ )
self.assertNotIn('decoder_attention_mask', lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : int = [
'Summary of the text.',
'Another summary.',
]
lowerCamelCase__ : str = tokenizer(
text_target=lowerCamelCase_, max_length=3_2, padding='max_length', truncation=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertEqual(3_2, targets['input_ids'].shape[1] )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length, 4_2 )
# Now let's start the test
lowerCamelCase__ : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : str = ' He is very happy, UNwant\u00E9d,running'
lowerCamelCase__ : str = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : str = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
shutil.rmtree(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : Union[str, Any] = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
lowerCamelCase__ : List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
lowerCamelCase__ : List[str] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : int = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Tuple = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertIn('new_additional_special_token', after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length, 4_2 )
lowerCamelCase__ : List[Any] = tokenizer.__class__.from_pretrained(lowerCamelCase_, model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length, 4_3 )
shutil.rmtree(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : Optional[Any] = json.load(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : List[str] = json.load(lowerCamelCase_ )
lowerCamelCase__ : Any = [f'''<extra_id_{i}>''' for i in range(1_2_5 )]
lowerCamelCase__ : Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
lowerCamelCase__ : List[str] = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCamelCase__ : Dict = tokenizer_class.from_pretrained(
lowerCamelCase_, )
self.assertIn(
'an_additional_special_token', tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['an_additional_special_token'], tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ), )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCamelCase__ : Optional[Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token', lstrip=lowerCamelCase_ )]
lowerCamelCase__ : Any = tokenizer_class.from_pretrained(
lowerCamelCase_, additional_special_tokens=lowerCamelCase_, )
self.assertIn('a_new_additional_special_token', tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'], tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ), )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ), '�' )
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.get_tokenizers(fast=lowerCamelCase_, do_lower_case=lowerCamelCase_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase__ : Tuple = ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
lowerCamelCase__ : List[str] = tokenizer.convert_tokens_to_string(lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[Any] = botoa.client('iam' )
lowerCamelCase__ : Any = {
'Version': '2012-10-17',
'Statement': [
{'Effect': 'Allow', 'Principal': {'Service': 'sagemaker.amazonaws.com'}, 'Action': 'sts:AssumeRole'}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=_lowerCamelCase , AssumeRolePolicyDocument=json.dumps(_lowerCamelCase , indent=2 ) )
lowerCamelCase__ : Optional[int] = {
'Version': '2012-10-17',
'Statement': [
{
'Effect': 'Allow',
'Action': [
'sagemaker:*',
'ecr:GetDownloadUrlForLayer',
'ecr:BatchGetImage',
'ecr:BatchCheckLayerAvailability',
'ecr:GetAuthorizationToken',
'cloudwatch:PutMetricData',
'cloudwatch:GetMetricData',
'cloudwatch:GetMetricStatistics',
'cloudwatch:ListMetrics',
'logs:CreateLogGroup',
'logs:CreateLogStream',
'logs:DescribeLogStreams',
'logs:PutLogEvents',
'logs:GetLogEvents',
's3:CreateBucket',
's3:ListBucket',
's3:GetBucketLocation',
's3:GetObject',
's3:PutObject',
],
'Resource': '*',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=_lowerCamelCase , PolicyName=f'''{role_name}_policy_permission''' , PolicyDocument=json.dumps(_lowerCamelCase , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f'''role {role_name} already exists. Using existing one''' )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[int] = botoa.client('iam' )
return iam_client.get_role(RoleName=_lowerCamelCase )["Role"]["Arn"]
def lowerCamelCase_ ( ):
lowerCamelCase__ : List[str] = _ask_options(
'How do you want to authorize?' , ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '] , _lowerCamelCase , )
lowerCamelCase__ : int = None
if credentials_configuration == 0:
lowerCamelCase__ : Union[str, Any] = _ask_field('Enter your AWS Profile name: [default] ' , default='default' )
lowerCamelCase__ : List[Any] = aws_profile
else:
print(
'Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'
'`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`' )
lowerCamelCase__ : Tuple = _ask_field('AWS Access Key ID: ' )
lowerCamelCase__ : Union[str, Any] = aws_access_key_id
lowerCamelCase__ : List[str] = _ask_field('AWS Secret Access Key: ' )
lowerCamelCase__ : Optional[int] = aws_secret_access_key
lowerCamelCase__ : str = _ask_field('Enter your AWS Region: [us-east-1]' , default='us-east-1' )
lowerCamelCase__ : Any = aws_region
lowerCamelCase__ : Union[str, Any] = _ask_options(
'Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?' , ['Provide IAM Role name', 'Create new IAM role using credentials'] , _lowerCamelCase , )
if role_management == 0:
lowerCamelCase__ : Union[str, Any] = _ask_field('Enter your IAM role name: ' )
else:
lowerCamelCase__ : Optional[Any] = 'accelerate_sagemaker_execution_role'
print(f'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials''' )
_create_iam_role_for_sagemaker(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = _ask_field(
'Do you want to use custom Docker image? [yes/NO]: ' , _convert_yes_no_to_bool , default=_lowerCamelCase , error_message='Please enter yes or no.' , )
lowerCamelCase__ : int = None
if is_custom_docker_image:
lowerCamelCase__ : List[Any] = _ask_field('Enter your Docker image: ' , lambda _lowerCamelCase : str(_lowerCamelCase ).lower() )
lowerCamelCase__ : Optional[Any] = _ask_field(
'Do you want to provide SageMaker input channels with data locations? [yes/NO]: ' , _convert_yes_no_to_bool , default=_lowerCamelCase , error_message='Please enter yes or no.' , )
lowerCamelCase__ : Optional[int] = None
if is_sagemaker_inputs_enabled:
lowerCamelCase__ : Dict = _ask_field(
'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ' , lambda _lowerCamelCase : str(_lowerCamelCase ).lower() , )
lowerCamelCase__ : str = _ask_field(
'Do you want to enable SageMaker metrics? [yes/NO]: ' , _convert_yes_no_to_bool , default=_lowerCamelCase , error_message='Please enter yes or no.' , )
lowerCamelCase__ : Tuple = None
if is_sagemaker_metrics_enabled:
lowerCamelCase__ : Union[str, Any] = _ask_field(
'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ' , lambda _lowerCamelCase : str(_lowerCamelCase ).lower() , )
lowerCamelCase__ : Tuple = _ask_options(
'What is the distributed mode?' , ['No distributed training', 'Data parallelism'] , _convert_sagemaker_distributed_mode , )
lowerCamelCase__ : List[str] = {}
lowerCamelCase__ : Union[str, Any] = _ask_field(
'Do you wish to optimize your script with torch dynamo?[yes/NO]:' , _convert_yes_no_to_bool , default=_lowerCamelCase , error_message='Please enter yes or no.' , )
if use_dynamo:
lowerCamelCase__ : Tuple = 'dynamo_'
lowerCamelCase__ : Optional[int] = _ask_options(
'Which dynamo backend would you like to use?' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
lowerCamelCase__ : List[Any] = _ask_field(
'Do you want to customize the defaults sent to torch.compile? [yes/NO]: ' , _convert_yes_no_to_bool , default=_lowerCamelCase , error_message='Please enter yes or no.' , )
if use_custom_options:
lowerCamelCase__ : List[Any] = _ask_options(
'Which mode do you want to use?' , _lowerCamelCase , lambda _lowerCamelCase : TORCH_DYNAMO_MODES[int(_lowerCamelCase )] , default='default' , )
lowerCamelCase__ : Optional[int] = _ask_field(
'Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ' , _convert_yes_no_to_bool , default=_lowerCamelCase , error_message='Please enter yes or no.' , )
lowerCamelCase__ : Any = _ask_field(
'Do you want to enable dynamic shape tracing? [yes/NO]: ' , _convert_yes_no_to_bool , default=_lowerCamelCase , error_message='Please enter yes or no.' , )
lowerCamelCase__ : Optional[Any] = 'Which EC2 instance type you want to use for your training?'
if distributed_type != SageMakerDistributedType.NO:
lowerCamelCase__ : Optional[int] = _ask_options(
_lowerCamelCase , _lowerCamelCase , lambda _lowerCamelCase : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_lowerCamelCase )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
lowerCamelCase__ : Tuple = _ask_field(_lowerCamelCase , lambda _lowerCamelCase : str(_lowerCamelCase ).lower() , default='ml.p3.2xlarge' )
lowerCamelCase__ : Optional[Any] = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
lowerCamelCase__ : List[str] = _ask_field(
'How many machines do you want use? [1]: ' , _lowerCamelCase , default=1 , )
lowerCamelCase__ : Tuple = _ask_options(
'Do you wish to use FP16 or BF16 (mixed precision)?' , ['no', 'fp16', 'bf16', 'fp8'] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.' )
return SageMakerConfig(
image_uri=_lowerCamelCase , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=_lowerCamelCase , use_cpu=_lowerCamelCase , dynamo_config=_lowerCamelCase , eca_instance_type=_lowerCamelCase , profile=_lowerCamelCase , region=_lowerCamelCase , iam_role_name=_lowerCamelCase , mixed_precision=_lowerCamelCase , num_machines=_lowerCamelCase , sagemaker_inputs_file=_lowerCamelCase , sagemaker_metrics_file=_lowerCamelCase , )
| 696 |
"""simple docstring"""
from math import pi, sqrt, tan
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
lowerCamelCase__ : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_lowerCamelCase , 2 ) * torus_radius * tube_radius
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
lowerCamelCase__ : Dict = (sidea + sidea + sidea) / 2
lowerCamelCase__ : str = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 696 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a_ ( snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Any = StableDiffusionXLImgaImgPipeline
lowerCamelCase__ : Dict = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
lowerCamelCase__ : Any = PipelineTesterMixin.required_optional_params - {'latents'}
lowerCamelCase__ : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCamelCase__ : Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase__ : Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Optional[int] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=4, out_channels=4, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'), up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'), attention_head_dim=(2, 4), use_linear_projection=lowerCamelCase_, addition_embed_type='text_time', addition_time_embed_dim=8, transformer_layers_per_block=(1, 2), projection_class_embeddings_input_dim=8_0, cross_attention_dim=6_4, )
lowerCamelCase__ : Optional[int] = EulerDiscreteScheduler(
beta_start=0.00_085, beta_end=0.012, steps_offset=1, beta_schedule='scaled_linear', timestep_spacing='leading', )
torch.manual_seed(0 )
lowerCamelCase__ : Optional[Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4], in_channels=3, out_channels=3, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'], up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'], latent_channels=4, sample_size=1_2_8, )
torch.manual_seed(0 )
lowerCamelCase__ : Optional[int] = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=3_2, intermediate_size=3_7, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, hidden_act='gelu', projection_dim=3_2, )
lowerCamelCase__ : Optional[Any] = CLIPTextModel(lowerCamelCase_ )
lowerCamelCase__ : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip', local_files_only=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = CLIPTextModelWithProjection(lowerCamelCase_ )
lowerCamelCase__ : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip', local_files_only=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'text_encoder_2': text_encoder_a,
'tokenizer_2': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def a__ (self, lowerCamelCase_, lowerCamelCase_=0 ):
'''simple docstring'''
lowerCamelCase__ : int = floats_tensor((1, 3, 3_2, 3_2), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
lowerCamelCase__ : str = image / 2 + 0.5
if str(lowerCamelCase_ ).startswith('mps' ):
lowerCamelCase__ : str = torch.manual_seed(lowerCamelCase_ )
else:
lowerCamelCase__ : str = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
lowerCamelCase__ : str = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 5.0,
'output_type': 'numpy',
'strength': 0.75,
}
return inputs
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : Optional[int] = self.get_dummy_components()
lowerCamelCase__ : Optional[Any] = StableDiffusionXLImgaImgPipeline(**lowerCamelCase_ )
lowerCamelCase__ : str = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : str = self.get_dummy_inputs(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = sd_pipe(**lowerCamelCase_ ).images
lowerCamelCase__ : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowerCamelCase__ : Tuple = np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def a__ (self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def a__ (self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.get_dummy_components()
lowerCamelCase__ : Tuple = StableDiffusionXLImgaImgPipeline(**lowerCamelCase_ )
lowerCamelCase__ : List[str] = sd_pipe.to(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
# forward without prompt embeds
lowerCamelCase__ : Tuple = self.get_dummy_inputs(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = 3 * ['this is a negative prompt']
lowerCamelCase__ : Optional[Any] = negative_prompt
lowerCamelCase__ : Any = 3 * [inputs['prompt']]
lowerCamelCase__ : int = sd_pipe(**lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
lowerCamelCase__ : List[Any] = self.get_dummy_inputs(lowerCamelCase_ )
lowerCamelCase__ : Any = 3 * ['this is a negative prompt']
lowerCamelCase__ : Any = 3 * [inputs.pop('prompt' )]
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : Optional[int] = sd_pipe.encode_prompt(lowerCamelCase_, negative_prompt=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = sd_pipe(
**lowerCamelCase_, prompt_embeds=lowerCamelCase_, negative_prompt_embeds=lowerCamelCase_, pooled_prompt_embeds=lowerCamelCase_, negative_pooled_prompt_embeds=lowerCamelCase_, )
lowerCamelCase__ : Any = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ (self, lowerCamelCase_, lowerCamelCase_="cpu", lowerCamelCase_=torch.floataa, lowerCamelCase_=0 ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = np.random.RandomState(lowerCamelCase_ ).standard_normal((1, 4, 6_4, 6_4) )
lowerCamelCase__ : Tuple = torch.from_numpy(lowerCamelCase_ ).to(device=lowerCamelCase_, dtype=lowerCamelCase_ )
lowerCamelCase__ : Dict = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base' )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : List[Any] = self.get_inputs(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = pipe(**lowerCamelCase_ ).images
lowerCamelCase__ : str = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : Optional[int] = np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506] )
assert np.abs(image_slice - expected_slice ).max() < 7e-3
| 696 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=9_9, lowerCamelCase_=6_4, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Dict = parent
lowerCamelCase__ : Tuple = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : List[Any] = is_training
lowerCamelCase__ : str = use_input_mask
lowerCamelCase__ : Optional[Any] = use_token_type_ids
lowerCamelCase__ : Any = use_labels
lowerCamelCase__ : Optional[int] = vocab_size
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : Optional[int] = num_hidden_layers
lowerCamelCase__ : List[Any] = num_attention_heads
lowerCamelCase__ : Union[str, Any] = intermediate_size
lowerCamelCase__ : List[str] = hidden_act
lowerCamelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCamelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCamelCase__ : Dict = max_position_embeddings
lowerCamelCase__ : Dict = type_vocab_size
lowerCamelCase__ : Union[str, Any] = type_sequence_label_size
lowerCamelCase__ : List[Any] = initializer_range
lowerCamelCase__ : List[Any] = num_labels
lowerCamelCase__ : Union[str, Any] = num_choices
lowerCamelCase__ : List[str] = scope
lowerCamelCase__ : Dict = vocab_size - 1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Optional[Any] = None
if self.use_input_mask:
lowerCamelCase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Any = None
if self.use_labels:
lowerCamelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : str = self.get_config()
return config, input_ids, input_mask, token_labels
def a__ (self ):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=lowerCamelCase_, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[str] = self.prepare_config_and_inputs()
lowerCamelCase__ : Optional[Any] = True
return config, input_ids, input_mask, token_labels
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = GPTNeoXModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.num_labels
lowerCamelCase__ : Optional[Any] = GPTNeoXForQuestionAnswering(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : str = self.num_labels
lowerCamelCase__ : Optional[int] = GPTNeoXForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : List[Any] = GPTNeoXForTokenClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Tuple = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = True
lowerCamelCase__ : List[str] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
# first forward pass
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, use_cache=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase__ : str = ids_tensor((self.batch_size, 3), config.vocab_size )
lowerCamelCase__ : List[Any] = ids_tensor((self.batch_size, 3), vocab_size=2 )
# append to next input_ids and
lowerCamelCase__ : Tuple = torch.cat([input_ids, next_tokens], dim=-1 )
lowerCamelCase__ : Tuple = torch.cat([input_mask, next_mask], dim=-1 )
lowerCamelCase__ : List[str] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, output_hidden_states=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = output_from_no_past['hidden_states'][0]
lowerCamelCase__ : Optional[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, past_key_values=lowerCamelCase_, output_hidden_states=lowerCamelCase_, )['hidden_states'][0]
# select random slice
lowerCamelCase__ : Dict = ids_tensor((1,), output_from_past.shape[-1] ).item()
lowerCamelCase__ : Optional[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-3 ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = config_and_inputs
lowerCamelCase__ : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : int = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ : Dict = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Optional[int] = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = GPTNeoXModelTester(self )
lowerCamelCase__ : Union[str, Any] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=6_4, num_attention_heads=8 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCamelCase__ : Optional[Any] = None
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
@unittest.skip(reason='Feed forward chunking is not implemented' )
def a__ (self ):
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Optional[Any] = ids_tensor([1, 1_0], config.vocab_size )
lowerCamelCase__ : Tuple = ids_tensor([1, int(config.max_position_embeddings * 1.5 )], config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Any = GPTNeoXModel(lowerCamelCase_ )
original_model.to(lowerCamelCase_ )
original_model.eval()
lowerCamelCase__ : List[Any] = original_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = original_model(lowerCamelCase_ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Optional[int] = {'type': scaling_type, 'factor': 10.0}
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
scaled_model.to(lowerCamelCase_ )
scaled_model.eval()
lowerCamelCase__ : Tuple = scaled_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = scaled_model(lowerCamelCase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
@require_torch
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' )
for checkpointing in [True, False]:
lowerCamelCase__ : Optional[Any] = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = tokenizer('My favorite food is', return_tensors='pt' ).to(lowerCamelCase_ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
lowerCamelCase__ : Dict = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
lowerCamelCase__ : Dict = model.generate(**lowerCamelCase_, do_sample=lowerCamelCase_, max_new_tokens=2_0 )
lowerCamelCase__ : Optional[Any] = tokenizer.batch_decode(lowerCamelCase_ )[0]
self.assertEqual(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A_ : Union[str, Any] = logging.get_logger()
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = True ):
print(f'''Converting {name}...''' )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
lowerCamelCase__ : List[str] = timm.create_model('levit_128s' , pretrained=_lowerCamelCase )
else:
lowerCamelCase__ : Dict = timm.create_model('levit_128' , pretrained=_lowerCamelCase )
if hidden_sizes == 192:
lowerCamelCase__ : Optional[int] = timm.create_model('levit_192' , pretrained=_lowerCamelCase )
if hidden_sizes == 256:
lowerCamelCase__ : Dict = timm.create_model('levit_256' , pretrained=_lowerCamelCase )
if hidden_sizes == 384:
lowerCamelCase__ : List[str] = timm.create_model('levit_384' , pretrained=_lowerCamelCase )
from_model.eval()
lowerCamelCase__ : int = LevitForImageClassificationWithTeacher(_lowerCamelCase ).eval()
lowerCamelCase__ : Optional[int] = OrderedDict()
lowerCamelCase__ : Optional[Any] = from_model.state_dict()
lowerCamelCase__ : int = list(from_model.state_dict().keys() )
lowerCamelCase__ : List[str] = list(our_model.state_dict().keys() )
print(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
for i in range(len(_lowerCamelCase ) ):
lowerCamelCase__ : Optional[int] = weights[og_keys[i]]
our_model.load_state_dict(_lowerCamelCase )
lowerCamelCase__ : List[Any] = torch.randn((2, 3, 224, 224) )
lowerCamelCase__ : Any = from_model(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = our_model(_lowerCamelCase ).logits
assert torch.allclose(_lowerCamelCase , _lowerCamelCase ), "The model logits don't match the original one."
lowerCamelCase__ : Optional[int] = name
print(_lowerCamelCase )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
lowerCamelCase__ : Any = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f'''Pushed {checkpoint_name}''' )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = True ):
lowerCamelCase__ : Tuple = 'imagenet-1k-id2label.json'
lowerCamelCase__ : Dict = 1000
lowerCamelCase__ : str = (1, num_labels)
lowerCamelCase__ : List[str] = 'huggingface/label-files'
lowerCamelCase__ : List[str] = num_labels
lowerCamelCase__ : Optional[int] = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type='dataset' ) , 'r' ) )
lowerCamelCase__ : Any = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ : Union[str, Any] = idalabel
lowerCamelCase__ : List[Any] = {v: k for k, v in idalabel.items()}
lowerCamelCase__ : List[str] = partial(_lowerCamelCase , num_labels=_lowerCamelCase , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase )
lowerCamelCase__ : Dict = {
'levit-128S': 128,
'levit-128': 128,
'levit-192': 192,
'levit-256': 256,
'levit-384': 384,
}
lowerCamelCase__ : List[str] = {
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , _lowerCamelCase , names_to_config[model_name] , _lowerCamelCase , _lowerCamelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return config, expected_shape
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="levit-dump-folder/",
type=Path,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
A_ : Tuple = parser.parse_args()
A_ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 696 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A_ : Dict = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
A_ : List[Any] = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
A_ : Union[str, Any] = spec.loader.load_module()
A_ : int = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
A_ : Optional[int] = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
A_ : str = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def lowerCamelCase_ ( ):
lowerCamelCase__ : Dict = []
for config_class in list(CONFIG_MAPPING.values() ):
lowerCamelCase__ : Dict = False
# source code of `config_class`
lowerCamelCase__ : str = inspect.getsource(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = _re_checkpoint.findall(_lowerCamelCase )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
lowerCamelCase__ : Any = f'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
lowerCamelCase__ : Any = True
break
lowerCamelCase__ : Dict = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
lowerCamelCase__ : Optional[Any] = '\n'.join(sorted(_lowerCamelCase ) )
raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 696 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
A_ : List[Any] = logging.get_logger(__name__)
A_ : Any = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
A_ : List[Any] = {
"vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
"tokenizer_file": {
"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
},
}
A_ : Dict = {"mobilebert-uncased": 5_12}
A_ : int = {}
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = VOCAB_FILES_NAMES
lowerCamelCase__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase__ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : int = MobileBertTokenizer
def __init__(self, lowerCamelCase_=None, lowerCamelCase_=None, lowerCamelCase_=True, lowerCamelCase_="[UNK]", lowerCamelCase_="[SEP]", lowerCamelCase_="[PAD]", lowerCamelCase_="[CLS]", lowerCamelCase_="[MASK]", lowerCamelCase_=True, lowerCamelCase_=None, **lowerCamelCase_, ):
'''simple docstring'''
super().__init__(
lowerCamelCase_, tokenizer_file=lowerCamelCase_, do_lower_case=lowerCamelCase_, unk_token=lowerCamelCase_, sep_token=lowerCamelCase_, pad_token=lowerCamelCase_, cls_token=lowerCamelCase_, mask_token=lowerCamelCase_, tokenize_chinese_chars=lowerCamelCase_, strip_accents=lowerCamelCase_, **lowerCamelCase_, )
lowerCamelCase__ : Optional[int] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase', lowerCamelCase_ ) != do_lower_case
or normalizer_state.get('strip_accents', lowerCamelCase_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars', lowerCamelCase_ ) != tokenize_chinese_chars
):
lowerCamelCase__ : Optional[Any] = getattr(lowerCamelCase_, normalizer_state.pop('type' ) )
lowerCamelCase__ : Optional[int] = do_lower_case
lowerCamelCase__ : Tuple = strip_accents
lowerCamelCase__ : str = tokenize_chinese_chars
lowerCamelCase__ : int = normalizer_class(**lowerCamelCase_ )
lowerCamelCase__ : str = do_lower_case
def a__ (self, lowerCamelCase_, lowerCamelCase_=None ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def a__ (self, lowerCamelCase_, lowerCamelCase_ = None ):
'''simple docstring'''
lowerCamelCase__ : Dict = [self.sep_token_id]
lowerCamelCase__ : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a__ (self, lowerCamelCase_, lowerCamelCase_ = None ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self._tokenizer.model.save(lowerCamelCase_, name=lowerCamelCase_ )
return tuple(lowerCamelCase_ )
| 696 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = ["LlamaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = ["LlamaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"LlamaForCausalLM",
"LlamaModel",
"LlamaPreTrainedModel",
"LlamaForSequenceClassification",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
A_ : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError('check_bouncy() accepts only integer arguments' )
lowerCamelCase__ : List[Any] = str(_lowerCamelCase )
lowerCamelCase__ : str = ''.join(sorted(_lowerCamelCase ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != str_n
def lowerCamelCase_ ( _lowerCamelCase = 99 ):
if not 0 < percent < 100:
raise ValueError('solution() only accepts values from 0 to 100' )
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Optional[Any] = 1
while True:
if check_bouncy(_lowerCamelCase ):
bouncy_num += 1
if (bouncy_num / num) * 100 >= percent:
return num
num += 1
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"{solution(99)}")
| 696 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A_ : Optional[int] = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A_ : List[str] = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
for data in res.iter_content(1_00_00):
out_file.write(data)
A_ : Tuple = BeautifulSoup(res.text, "html.parser")
A_ : Dict = list(soup.select(".eZt8xd"))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("href"))
else:
webbrowser.open(f"https://google.com{link.get('href')}")
| 696 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
"ConditionalDetrOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = ["ConditionalDetrFeatureExtractor"]
A_ : str = ["ConditionalDetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Any = [
"CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
A_ : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
lowerCamelCase__ : Tuple = FlaxDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=lowerCamelCase_, cache_dir=lowerCamelCase_ )
lowerCamelCase__ : List[str] = [t[-1] for t in os.walk(os.path.join(lowerCamelCase_, os.listdir(lowerCamelCase_ )[0], 'snapshots' ) )]
lowerCamelCase__ : Optional[int] = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('.bin' ) for f in files )
@slow
@require_flax
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Any = FlaxStableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Any = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Optional[int] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Any = 4
lowerCamelCase__ : Any = jax.device_count()
lowerCamelCase__ : List[Any] = num_samples * [prompt]
lowerCamelCase__ : Optional[int] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : int = replicate(lowerCamelCase_ )
lowerCamelCase__ : Any = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : int = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 4.1_514_745 ) < 1e-3
assert np.abs(np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 49_947.875 ) < 5e-1
lowerCamelCase__ : Union[str, Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(lowerCamelCase_ ) == num_samples
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='flax', safety_checker=lowerCamelCase_ )
lowerCamelCase__ : int = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : List[str] = jax.random.PRNGKey(0 )
lowerCamelCase__ : int = 5_0
lowerCamelCase__ : List[str] = jax.device_count()
lowerCamelCase__ : Dict = num_samples * [prompt]
lowerCamelCase__ : List[str] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Dict = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = shard(lowerCamelCase_ )
lowerCamelCase__ : str = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.05_652_401) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_383_808.2) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : List[Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Union[str, Any] = 5_0
lowerCamelCase__ : Any = jax.device_count()
lowerCamelCase__ : Tuple = num_samples * [prompt]
lowerCamelCase__ : List[str] = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Any = replicate(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : int = shard(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.04_003_906) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_373_516.75) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Tuple = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa )
lowerCamelCase__ : Tuple = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Union[str, Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : Optional[Any] = 5_0
lowerCamelCase__ : Tuple = jax.device_count()
lowerCamelCase__ : Optional[int] = num_samples * [prompt]
lowerCamelCase__ : str = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Optional[int] = replicate(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : List[str] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.04_003_906) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_373_516.75) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = FlaxDDIMScheduler(
beta_start=0.00_085, beta_end=0.012, beta_schedule='scaled_linear', set_alpha_to_one=lowerCamelCase_, steps_offset=1, )
lowerCamelCase__ , lowerCamelCase__ : List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, scheduler=lowerCamelCase_, safety_checker=lowerCamelCase_, )
lowerCamelCase__ : List[str] = scheduler.create_state()
lowerCamelCase__ : int = scheduler_state
lowerCamelCase__ : Any = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : Optional[Any] = jax.random.PRNGKey(0 )
lowerCamelCase__ : int = 5_0
lowerCamelCase__ : Optional[Any] = jax.device_count()
lowerCamelCase__ : Any = num_samples * [prompt]
lowerCamelCase__ : Any = pipeline.prepare_inputs(lowerCamelCase_ )
# shard inputs and rng
lowerCamelCase__ : Union[str, Any] = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = jax.random.split(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Dict = shard(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.045_043_945) ) < 1e-3
assert np.abs((np.abs(lowerCamelCase_, dtype=np.floataa ).sum() - 2_347_693.5) ) < 5e-1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
' field, close up, split lighting, cinematic'
)
lowerCamelCase__ : int = jax.device_count()
lowerCamelCase__ : Dict = num_samples * [prompt]
lowerCamelCase__ : str = jax.random.split(jax.random.PRNGKey(0 ), lowerCamelCase_ )
lowerCamelCase__ , lowerCamelCase__ : List[str] = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_, )
lowerCamelCase__ : Union[str, Any] = replicate(lowerCamelCase_ )
lowerCamelCase__ : Dict = pipeline.prepare_inputs(lowerCamelCase_ )
lowerCamelCase__ : Tuple = shard(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : int = images[2, 0, 2_5_6, 1_0:1_7, 1]
# With memory efficient attention
lowerCamelCase__ , lowerCamelCase__ : str = FlaxStableDiffusionPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4', revision='bf16', dtype=jnp.bfloataa, safety_checker=lowerCamelCase_, use_memory_efficient_attention=lowerCamelCase_, )
lowerCamelCase__ : Dict = replicate(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = pipeline.prepare_inputs(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = shard(lowerCamelCase_ )
lowerCamelCase__ : Any = pipeline(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, jit=lowerCamelCase_ ).images
assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : Any = images[2, 0, 2_5_6, 1_0:1_7, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1e-2
| 696 | 1 |
"""simple docstring"""
A_ : Optional[Any] = "Alexander Joslin"
import operator as op
from .stack import Stack
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
lowerCamelCase__ : Stack[int] = Stack()
lowerCamelCase__ : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_lowerCamelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(_lowerCamelCase )
elif i == ")":
# RULE 4
lowerCamelCase__ : Optional[Any] = operator_stack.peek()
operator_stack.pop()
lowerCamelCase__ : Union[str, Any] = operand_stack.peek()
operand_stack.pop()
lowerCamelCase__ : List[Any] = operand_stack.peek()
operand_stack.pop()
lowerCamelCase__ : int = operators[opr](_lowerCamelCase , _lowerCamelCase )
operand_stack.push(_lowerCamelCase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
A_ : Optional[int] = "(5 + ((4 * 2) * (2 + 3)))"
# answer = 45
print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| 696 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A_ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowerCamelCase_, scheduler=lowerCamelCase_ )
@torch.no_grad()
def __call__(self, lowerCamelCase_ = 1, lowerCamelCase_ = 1_0_0, lowerCamelCase_ = None, lowerCamelCase_ = None, lowerCamelCase_ = True, ):
'''simple docstring'''
if audio_length_in_s is None:
lowerCamelCase__ : str = self.unet.config.sample_size / self.unet.config.sample_rate
lowerCamelCase__ : Optional[Any] = audio_length_in_s * self.unet.config.sample_rate
lowerCamelCase__ : str = 2 ** len(self.unet.up_blocks )
if sample_size < 3 * down_scale_factor:
raise ValueError(
f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
f''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )
lowerCamelCase__ : Dict = int(lowerCamelCase_ )
if sample_size % down_scale_factor != 0:
lowerCamelCase__ : Union[str, Any] = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
' process.' )
lowerCamelCase__ : Optional[Any] = int(lowerCamelCase_ )
lowerCamelCase__ : List[str] = next(iter(self.unet.parameters() ) ).dtype
lowerCamelCase__ : Union[str, Any] = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(lowerCamelCase_, lowerCamelCase_ ) and len(lowerCamelCase_ ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(lowerCamelCase_ )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowerCamelCase__ : Union[str, Any] = randn_tensor(lowerCamelCase_, generator=lowerCamelCase_, device=self.device, dtype=lowerCamelCase_ )
# set step values
self.scheduler.set_timesteps(lowerCamelCase_, device=audio.device )
lowerCamelCase__ : int = self.scheduler.timesteps.to(lowerCamelCase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowerCamelCase__ : List[Any] = self.unet(lowerCamelCase_, lowerCamelCase_ ).sample
# 2. compute previous image: x_t -> t_t-1
lowerCamelCase__ : List[str] = self.scheduler.step(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ).prev_sample
lowerCamelCase__ : Union[str, Any] = audio.clamp(-1, 1 ).float().cpu().numpy()
lowerCamelCase__ : Tuple = audio[:, :, :original_sample_size]
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A_ : Dict = "pt"
elif is_tf_available():
A_ : Union[str, Any] = "tf"
else:
A_ : List[str] = "jax"
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = PerceiverTokenizer
lowerCamelCase__ : Optional[Any] = False
def a__ (self ):
'''simple docstring'''
super().setUp()
lowerCamelCase__ : int = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def a__ (self ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver' )
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname, **lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_=False, lowerCamelCase_=2_0, lowerCamelCase_=5 ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = []
for i in range(len(lowerCamelCase_ ) ):
try:
lowerCamelCase__ : Any = tokenizer.decode([i], clean_up_tokenization_spaces=lowerCamelCase_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCamelCase__ : Any = list(filter(lambda lowerCamelCase_ : re.match(r'^[ a-zA-Z]+$', t[1] ), lowerCamelCase_ ) )
lowerCamelCase__ : Union[str, Any] = list(filter(lambda lowerCamelCase_ : [t[0]] == tokenizer.encode(t[1], add_special_tokens=lowerCamelCase_ ), lowerCamelCase_ ) )
if max_length is not None and len(lowerCamelCase_ ) > max_length:
lowerCamelCase__ : int = toks[:max_length]
if min_length is not None and len(lowerCamelCase_ ) < min_length and len(lowerCamelCase_ ) > 0:
while len(lowerCamelCase_ ) < min_length:
lowerCamelCase__ : Dict = toks + toks
# toks_str = [t[1] for t in toks]
lowerCamelCase__ : int = [t[0] for t in toks]
# Ensure consistency
lowerCamelCase__ : Optional[int] = tokenizer.decode(lowerCamelCase_, clean_up_tokenization_spaces=lowerCamelCase_ )
if " " not in output_txt and len(lowerCamelCase_ ) > 1:
lowerCamelCase__ : List[Any] = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=lowerCamelCase_ )
+ ' '
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=lowerCamelCase_ )
)
if with_prefix_space:
lowerCamelCase__ : Optional[Any] = ' ' + output_txt
lowerCamelCase__ : List[Any] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
return output_txt, output_ids
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = 'Unicode €.'
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_ )
lowerCamelCase__ : Dict = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : int = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]Unicode €.[SEP]' )
lowerCamelCase__ : List[str] = tokenizer('e è é ê ë' )
lowerCamelCase__ : Dict = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : Any = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]e è é ê ë[SEP]' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ), '[CLS]e è é ê ë[SEP]' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
lowerCamelCase__ : List[Any] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
if FRAMEWORK != "jax":
lowerCamelCase__ : List[str] = list(batch.input_ids.numpy()[0] )
else:
lowerCamelCase__ : int = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertEqual((2, 3_8), batch.input_ids.shape )
self.assertEqual((2, 3_8), batch.attention_mask.shape )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.perceiver_tokenizer
lowerCamelCase__ : List[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCamelCase__ : List[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids', lowerCamelCase_ )
self.assertIn('attention_mask', lowerCamelCase_ )
self.assertNotIn('decoder_input_ids', lowerCamelCase_ )
self.assertNotIn('decoder_attention_mask', lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : int = [
'Summary of the text.',
'Another summary.',
]
lowerCamelCase__ : str = tokenizer(
text_target=lowerCamelCase_, max_length=3_2, padding='max_length', truncation=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertEqual(3_2, targets['input_ids'].shape[1] )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length, 4_2 )
# Now let's start the test
lowerCamelCase__ : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : str = ' He is very happy, UNwant\u00E9d,running'
lowerCamelCase__ : str = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : str = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
shutil.rmtree(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : Union[str, Any] = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
lowerCamelCase__ : List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
lowerCamelCase__ : List[str] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : int = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Tuple = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertIn('new_additional_special_token', after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length, 4_2 )
lowerCamelCase__ : List[Any] = tokenizer.__class__.from_pretrained(lowerCamelCase_, model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length, 4_3 )
shutil.rmtree(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : Optional[Any] = json.load(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : List[str] = json.load(lowerCamelCase_ )
lowerCamelCase__ : Any = [f'''<extra_id_{i}>''' for i in range(1_2_5 )]
lowerCamelCase__ : Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
lowerCamelCase__ : List[str] = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCamelCase__ : Dict = tokenizer_class.from_pretrained(
lowerCamelCase_, )
self.assertIn(
'an_additional_special_token', tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['an_additional_special_token'], tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ), )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCamelCase__ : Optional[Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token', lstrip=lowerCamelCase_ )]
lowerCamelCase__ : Any = tokenizer_class.from_pretrained(
lowerCamelCase_, additional_special_tokens=lowerCamelCase_, )
self.assertIn('a_new_additional_special_token', tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'], tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ), )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ), '�' )
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.get_tokenizers(fast=lowerCamelCase_, do_lower_case=lowerCamelCase_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase__ : Tuple = ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
lowerCamelCase__ : List[str] = tokenizer.convert_tokens_to_string(lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
| 696 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class a_ :
'''simple docstring'''
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return None
class a_ :
'''simple docstring'''
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return None
class a_ ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase_, 'tf', 1_2, **lowerCamelCase_ )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase_, 'pt', 1_2, **lowerCamelCase_ )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
from transformers import BertModel
lowerCamelCase__ : Union[str, Any] = ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
with NamedTemporaryFile(mode='w+t' ) as vocab_file:
vocab_file.write('\n'.join(lowerCamelCase_ ) )
vocab_file.flush()
lowerCamelCase__ : Tuple = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
lowerCamelCase__ : Optional[Any] = BertModel(BertConfig(vocab_size=len(lowerCamelCase_ ) ) )
model.save_pretrained(lowerCamelCase_ )
self._test_export(lowerCamelCase_, 'pt', 1_2, lowerCamelCase_ )
@require_tf
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCamelCase__ : Optional[Any] = self._test_export(lowerCamelCase_, 'tf', 1_2, **lowerCamelCase_ )
lowerCamelCase__ : Any = quantize(Path(lowerCamelCase_ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase_ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
lowerCamelCase__ : Any = self._test_export(lowerCamelCase_, 'pt', 1_2, **lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = quantize(lowerCamelCase_ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase_ ).stat().st_size:
self.fail('Quantized model is bigger than initial ONNX model' )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, **lowerCamelCase_ ):
'''simple docstring'''
try:
# Compute path
with TemporaryDirectory() as tempdir:
lowerCamelCase__ : str = Path(lowerCamelCase_ ).joinpath('model.onnx' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ )
return path
except Exception as e:
self.fail(lowerCamelCase_ )
@require_torch
@require_tokenizers
@slow
def a__ (self ):
'''simple docstring'''
from transformers import BertModel
lowerCamelCase__ : str = BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowerCamelCase__ : Union[str, Any] = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(lowerCamelCase_, lowerCamelCase_, 'pt' )
@require_tf
@require_tokenizers
@slow
def a__ (self ):
'''simple docstring'''
from transformers import TFBertModel
lowerCamelCase__ : Dict = TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random' ) )
lowerCamelCase__ : Optional[int] = BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random' )
self._test_infer_dynamic_axis(lowerCamelCase_, lowerCamelCase_, 'tf' )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = FeatureExtractionPipeline(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = infer_shapes(lowerCamelCase_, lowerCamelCase_ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase_ ), len(lowerCamelCase_ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3], lowerCamelCase_ )
self.assertSequenceEqual(variable_names[3:], lowerCamelCase_ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name], {0: 'batch', 1: 'sequence'} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['output_0'], {0: 'batch', 1: 'sequence'} )
self.assertDictEqual(shapes['output_1'], {0: 'batch'} )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = ['input_ids', 'attention_mask', 'token_type_ids']
lowerCamelCase__ : Optional[int] = {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
lowerCamelCase__ , lowerCamelCase__ : str = ensure_valid_input(FuncContiguousArgs(), lowerCamelCase_, lowerCamelCase_ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase_ ), 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase_ ), set(lowerCamelCase_ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase_, (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
lowerCamelCase__ , lowerCamelCase__ : Any = ensure_valid_input(FuncNonContiguousArgs(), lowerCamelCase_, lowerCamelCase_ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase_ ), 1 )
self.assertEqual(len(lowerCamelCase_ ), 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0], tokens['input_ids'] )
self.assertEqual(ordered_input_names[0], 'input_ids' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = generate_identified_filename(Path('/home/something/my_fake_model.onnx' ), '-test' )
self.assertEqual('/home/something/my_fake_model-test.onnx', generated.as_posix() )
| 696 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A_ : Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
A_ : List[Any] = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A_ : List[Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class a_ :
'''simple docstring'''
lowerCamelCase__ : Optional[str] = field(
default='cifar10' , metadata={'help': 'Name of a dataset from the datasets package'} )
lowerCamelCase__ : Optional[str] = field(
default=snake_case_ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
lowerCamelCase__ : Optional[str] = field(
default=snake_case_ , metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'} , )
lowerCamelCase__ : Optional[str] = field(default=snake_case_ , metadata={'help': 'A folder containing the training data.'} )
lowerCamelCase__ : Optional[str] = field(default=snake_case_ , metadata={'help': 'A folder containing the validation data.'} )
lowerCamelCase__ : Optional[float] = field(
default=0.1_5 , metadata={'help': 'Percent to split off of train for validation.'} )
lowerCamelCase__ : int = field(default=32 , metadata={'help': 'The size of the square patches to use for masking.'} )
lowerCamelCase__ : float = field(
default=0.6 , metadata={'help': 'Percentage of patches to mask.'} , )
lowerCamelCase__ : Optional[int] = field(
default=snake_case_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=snake_case_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = {}
if self.train_dir is not None:
lowerCamelCase__ : List[str] = self.train_dir
if self.validation_dir is not None:
lowerCamelCase__ : Dict = self.validation_dir
lowerCamelCase__ : Union[str, Any] = data_files if data_files else None
@dataclass
class a_ :
'''simple docstring'''
lowerCamelCase__ : str = field(
default=snake_case_ , metadata={
'help': (
'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
'checkpoint identifier on the hub. '
'Don\'t set if you want to train a model from scratch.'
)
} , )
lowerCamelCase__ : Optional[str] = field(
default=snake_case_ , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(snake_case_ )} , )
lowerCamelCase__ : Optional[str] = field(
default=snake_case_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCamelCase__ : Optional[str] = field(
default=snake_case_ , metadata={
'help': (
'Override some existing default config settings when a model is trained from scratch. Example: '
'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
)
} , )
lowerCamelCase__ : Optional[str] = field(
default=snake_case_ , metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'} , )
lowerCamelCase__ : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowerCamelCase__ : str = field(default=snake_case_ , metadata={'help': 'Name or path of preprocessor config.'} )
lowerCamelCase__ : bool = field(
default=snake_case_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=snake_case_ , metadata={
'help': (
'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=snake_case_ , metadata={
'help': (
'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=snake_case_ , metadata={'help': 'Stride to use for the encoder.'} , )
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_=1_9_2, lowerCamelCase_=3_2, lowerCamelCase_=4, lowerCamelCase_=0.6 ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = input_size
lowerCamelCase__ : Optional[int] = mask_patch_size
lowerCamelCase__ : Union[str, Any] = model_patch_size
lowerCamelCase__ : List[Any] = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError('Input size must be divisible by mask patch size' )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError('Mask patch size must be divisible by model patch size' )
lowerCamelCase__ : List[str] = self.input_size // self.mask_patch_size
lowerCamelCase__ : Optional[int] = self.mask_patch_size // self.model_patch_size
lowerCamelCase__ : Tuple = self.rand_size**2
lowerCamelCase__ : Any = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__(self ):
'''simple docstring'''
lowerCamelCase__ : Dict = np.random.permutation(self.token_count )[: self.mask_count]
lowerCamelCase__ : Dict = np.zeros(self.token_count, dtype=lowerCamelCase_ )
lowerCamelCase__ : Dict = 1
lowerCamelCase__ : List[str] = mask.reshape((self.rand_size, self.rand_size) )
lowerCamelCase__ : str = mask.repeat(self.scale, axis=0 ).repeat(self.scale, axis=1 )
return torch.tensor(mask.flatten() )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[str] = torch.stack([example['pixel_values'] for example in examples] )
lowerCamelCase__ : Optional[Any] = torch.stack([example['mask'] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def lowerCamelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCamelCase__ : int = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mim' , _lowerCamelCase , _lowerCamelCase )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCamelCase__ : Union[str, Any] = training_args.get_process_log_level()
logger.setLevel(_lowerCamelCase )
transformers.utils.logging.set_verbosity(_lowerCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowerCamelCase__ : List[str] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCamelCase__ : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
lowerCamelCase__ : Dict = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowerCamelCase__ : Optional[int] = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , _lowerCamelCase ) and data_args.train_val_split > 0.0:
lowerCamelCase__ : List[Any] = ds['train'].train_test_split(data_args.train_val_split )
lowerCamelCase__ : List[str] = split['train']
lowerCamelCase__ : Optional[Any] = split['test']
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase__ : Optional[int] = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
lowerCamelCase__ : Tuple = AutoConfig.from_pretrained(model_args.config_name_or_path , **_lowerCamelCase )
elif model_args.model_name_or_path:
lowerCamelCase__ : Optional[int] = AutoConfig.from_pretrained(model_args.model_name_or_path , **_lowerCamelCase )
else:
lowerCamelCase__ : Dict = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(f'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(f'''New config: {config}''' )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(_lowerCamelCase , 'decoder_type' ):
lowerCamelCase__ : Optional[int] = 'simmim'
# adapt config
lowerCamelCase__ : Optional[Any] = model_args.image_size if model_args.image_size is not None else config.image_size
lowerCamelCase__ : Any = model_args.patch_size if model_args.patch_size is not None else config.patch_size
lowerCamelCase__ : Any = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
'image_size': model_args.image_size,
'patch_size': model_args.patch_size,
'encoder_stride': model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
lowerCamelCase__ : Optional[Any] = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **_lowerCamelCase )
elif model_args.model_name_or_path:
lowerCamelCase__ : Optional[Any] = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **_lowerCamelCase )
else:
lowerCamelCase__ : Optional[Any] = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
lowerCamelCase__ : int = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
lowerCamelCase__ : Optional[Any] = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_lowerCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
lowerCamelCase__ : Dict = AutoModelForMaskedImageModeling.from_config(_lowerCamelCase )
if training_args.do_train:
lowerCamelCase__ : List[Any] = ds['train'].column_names
else:
lowerCamelCase__ : str = ds['validation'].column_names
if data_args.image_column_name is not None:
lowerCamelCase__ : List[Any] = data_args.image_column_name
elif "image" in column_names:
lowerCamelCase__ : Dict = 'image'
elif "img" in column_names:
lowerCamelCase__ : Optional[int] = 'img'
else:
lowerCamelCase__ : Union[str, Any] = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
lowerCamelCase__ : int = Compose(
[
Lambda(lambda _lowerCamelCase : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
lowerCamelCase__ : Dict = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(_lowerCamelCase ):
lowerCamelCase__ : str = [transforms(_lowerCamelCase ) for image in examples[image_column_name]]
lowerCamelCase__ : str = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
lowerCamelCase__ : int = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(_lowerCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
lowerCamelCase__ : Any = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(_lowerCamelCase )
# Initialize our trainer
lowerCamelCase__ : str = Trainer(
model=_lowerCamelCase , args=_lowerCamelCase , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=_lowerCamelCase , data_collator=_lowerCamelCase , )
# Training
if training_args.do_train:
lowerCamelCase__ : str = None
if training_args.resume_from_checkpoint is not None:
lowerCamelCase__ : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCamelCase__ : Tuple = last_checkpoint
lowerCamelCase__ : str = trainer.train(resume_from_checkpoint=_lowerCamelCase )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowerCamelCase__ : Optional[int] = trainer.evaluate()
trainer.log_metrics('eval' , _lowerCamelCase )
trainer.save_metrics('eval' , _lowerCamelCase )
# Write model card and (optionally) push to hub
lowerCamelCase__ : Dict = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'masked-image-modeling',
'dataset': data_args.dataset_name,
'tags': ['masked-image-modeling'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**_lowerCamelCase )
else:
trainer.create_model_card(**_lowerCamelCase )
if __name__ == "__main__":
main()
| 696 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : int = KandinskyVaaControlnetImgaImgPipeline
lowerCamelCase__ : Optional[int] = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
lowerCamelCase__ : Dict = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
lowerCamelCase__ : str = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowerCamelCase__ : Any = False
@property
def a__ (self ):
'''simple docstring'''
return 3_2
@property
def a__ (self ):
'''simple docstring'''
return 3_2
@property
def a__ (self ):
'''simple docstring'''
return self.time_input_dim
@property
def a__ (self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def a__ (self ):
'''simple docstring'''
return 1_0_0
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Optional[int] = {
'in_channels': 8,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image_hint',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
lowerCamelCase__ : int = UNetaDConditionModel(**lowerCamelCase_ )
return model
@property
def a__ (self ):
'''simple docstring'''
return {
"block_out_channels": [3_2, 3_2, 6_4, 6_4],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def a__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.dummy_unet
lowerCamelCase__ : List[Any] = self.dummy_movq
lowerCamelCase__ : Tuple = {
'num_train_timesteps': 1_0_0_0,
'beta_schedule': 'linear',
'beta_start': 0.00_085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
lowerCamelCase__ : Optional[Any] = DDIMScheduler(**lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def a__ (self, lowerCamelCase_, lowerCamelCase_=0 ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
lowerCamelCase__ : int = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1 ) ).to(
lowerCamelCase_ )
# create init_image
lowerCamelCase__ : Any = floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
lowerCamelCase__ : Dict = image.cpu().permute(0, 2, 3, 1 )[0]
lowerCamelCase__ : Optional[Any] = Image.fromarray(np.uinta(lowerCamelCase_ ) ).convert('RGB' ).resize((2_5_6, 2_5_6) )
# create hint
lowerCamelCase__ : Dict = floats_tensor((1, 3, 6_4, 6_4), rng=random.Random(lowerCamelCase_ ) ).to(lowerCamelCase_ )
if str(lowerCamelCase_ ).startswith('mps' ):
lowerCamelCase__ : int = torch.manual_seed(lowerCamelCase_ )
else:
lowerCamelCase__ : Any = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = {
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'hint': hint,
'generator': generator,
'height': 6_4,
'width': 6_4,
'num_inference_steps': 1_0,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = 'cpu'
lowerCamelCase__ : List[Any] = self.get_dummy_components()
lowerCamelCase__ : List[Any] = self.pipeline_class(**lowerCamelCase_ )
lowerCamelCase__ : Dict = pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : Any = pipe(**self.get_dummy_inputs(lowerCamelCase_ ) )
lowerCamelCase__ : List[Any] = output.images
lowerCamelCase__ : str = pipe(
**self.get_dummy_inputs(lowerCamelCase_ ), return_dict=lowerCamelCase_, )[0]
lowerCamelCase__ : int = image[0, -3:, -3:, -1]
lowerCamelCase__ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowerCamelCase__ : List[str] = np.array(
[0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy' )
lowerCamelCase__ : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
lowerCamelCase__ : Any = init_image.resize((5_1_2, 5_1_2) )
lowerCamelCase__ : List[str] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/hint_image_cat.png' )
lowerCamelCase__ : Any = torch.from_numpy(np.array(lowerCamelCase_ ) ).float() / 255.0
lowerCamelCase__ : Optional[int] = hint.permute(2, 0, 1 ).unsqueeze(0 )
lowerCamelCase__ : Union[str, Any] = 'A robot, 4k photo'
lowerCamelCase__ : Any = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior', torch_dtype=torch.floataa )
pipe_prior.to(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-controlnet-depth', torch_dtype=torch.floataa )
lowerCamelCase__ : int = pipeline.to(lowerCamelCase_ )
pipeline.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : str = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = pipe_prior(
lowerCamelCase_, image=lowerCamelCase_, strength=0.85, generator=lowerCamelCase_, negative_prompt='', ).to_tuple()
lowerCamelCase__ : Union[str, Any] = pipeline(
image=lowerCamelCase_, image_embeds=lowerCamelCase_, negative_image_embeds=lowerCamelCase_, hint=lowerCamelCase_, generator=lowerCamelCase_, num_inference_steps=1_0_0, height=5_1_2, width=5_1_2, strength=0.5, output_type='np', )
lowerCamelCase__ : Dict = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
# Return True if there is node that has not iterated.
lowerCamelCase__ : Optional[Any] = [False] * len(_lowerCamelCase )
lowerCamelCase__ : List[Any] = []
queue.append(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = True
while queue:
lowerCamelCase__ : Union[str, Any] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_lowerCamelCase )
lowerCamelCase__ : Dict = True
lowerCamelCase__ : Optional[Any] = u
return visited[t]
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
# This array is filled by BFS and to store path
lowerCamelCase__ : str = [-1] * (len(_lowerCamelCase ))
lowerCamelCase__ : str = 0
while bfs(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Optional[int] = float('Inf' )
lowerCamelCase__ : int = sink
while s != source:
# Find the minimum value in select path
lowerCamelCase__ : int = min(_lowerCamelCase , graph[parent[s]][s] )
lowerCamelCase__ : Tuple = parent[s]
max_flow += path_flow
lowerCamelCase__ : int = sink
while v != source:
lowerCamelCase__ : Optional[Any] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowerCamelCase__ : Dict = parent[v]
return max_flow
A_ : Union[str, Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
A_, A_ : Dict = 0, 5
print(ford_fulkerson(graph, source, sink))
| 696 |
"""simple docstring"""
A_ : List[str] = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dataclasses": "dataclasses",
"datasets": "datasets!=2.5.0",
"decord": "decord==0.6.0",
"deepspeed": "deepspeed>=0.9.3",
"diffusers": "diffusers",
"dill": "dill<0.3.5",
"evaluate": "evaluate>=0.2.0",
"fairscale": "fairscale>0.3",
"faiss-cpu": "faiss-cpu",
"fastapi": "fastapi",
"filelock": "filelock",
"flax": "flax>=0.4.1,<=0.7.0",
"ftfy": "ftfy",
"fugashi": "fugashi>=1.0",
"GitPython": "GitPython<3.1.19",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
"importlib_metadata": "importlib_metadata",
"ipadic": "ipadic>=1.0.0,<2.0",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
"jaxlib": "jaxlib>=0.1.65,<=0.4.13",
"jieba": "jieba",
"kenlm": "kenlm",
"keras-nlp": "keras-nlp>=0.3.1",
"librosa": "librosa",
"nltk": "nltk",
"natten": "natten>=0.14.6",
"numpy": "numpy>=1.17",
"onnxconverter-common": "onnxconverter-common",
"onnxruntime-tools": "onnxruntime-tools>=1.4.2",
"onnxruntime": "onnxruntime>=1.4.0",
"opencv-python": "opencv-python",
"optuna": "optuna",
"optax": "optax>=0.0.8,<=0.1.4",
"packaging": "packaging>=20.0",
"parameterized": "parameterized",
"phonemizer": "phonemizer",
"protobuf": "protobuf",
"psutil": "psutil",
"pyyaml": "pyyaml>=5.1",
"pydantic": "pydantic<2",
"pytest": "pytest>=7.2.0",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"python": "python>=3.8.0",
"ray[tune]": "ray[tune]",
"regex": "regex!=2019.12.17",
"requests": "requests",
"rhoknp": "rhoknp>=1.1.0,<1.3.1",
"rjieba": "rjieba",
"rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
"ruff": "ruff>=0.0.241,<=0.0.259",
"sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
"sacremoses": "sacremoses",
"safetensors": "safetensors>=0.3.1",
"sagemaker": "sagemaker>=2.31.0",
"scikit-learn": "scikit-learn",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"sigopt": "sigopt",
"starlette": "starlette",
"sudachipy": "sudachipy>=0.6.6",
"sudachidict_core": "sudachidict_core>=20220729",
"tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
"tensorflow": "tensorflow>=2.6,<2.14",
"tensorflow-text": "tensorflow-text<2.14",
"tf2onnx": "tf2onnx",
"timeout-decorator": "timeout-decorator",
"timm": "timm",
"tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
"torch": "torch>=1.9,!=1.12.0",
"torchaudio": "torchaudio",
"torchvision": "torchvision",
"pyctcdecode": "pyctcdecode>=0.4.0",
"tqdm": "tqdm>=4.27",
"unidic": "unidic>=1.0.2",
"unidic_lite": "unidic_lite>=1.0.7",
"urllib3": "urllib3<2.0.0",
"uvicorn": "uvicorn",
}
| 696 | 1 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
A_ : Union[str, Any] = "▁"
A_ : Dict = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Dict = BigBirdTokenizer
lowerCamelCase__ : str = BigBirdTokenizerFast
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : Optional[Any] = True
def a__ (self ):
'''simple docstring'''
super().setUp()
lowerCamelCase__ : List[Any] = self.tokenizer_class(lowerCamelCase_, keep_accents=lowerCamelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = '<s>'
lowerCamelCase__ : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_ ), lowerCamelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_ ), lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '<unk>' )
self.assertEqual(vocab_keys[1], '<s>' )
self.assertEqual(vocab_keys[-1], '[MASK]' )
self.assertEqual(len(lowerCamelCase_ ), 1_0_0_4 )
def a__ (self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size, 1_0_0_0 )
def a__ (self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCamelCase__ : Dict = self.get_tokenizer()
lowerCamelCase__ : List[Any] = self.get_rust_tokenizer()
lowerCamelCase__ : Union[str, Any] = 'I was born in 92000, and this is falsé.'
lowerCamelCase__ : List[str] = tokenizer.tokenize(lowerCamelCase_ )
lowerCamelCase__ : str = rust_tokenizer.tokenize(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = rust_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = self.get_rust_tokenizer()
lowerCamelCase__ : List[Any] = tokenizer.encode(lowerCamelCase_ )
lowerCamelCase__ : Any = rust_tokenizer.encode(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = BigBirdTokenizer(lowerCamelCase_, keep_accents=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = tokenizer.tokenize('This is a test' )
self.assertListEqual(lowerCamelCase_, ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase_ ), [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2], )
lowerCamelCase__ : str = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowerCamelCase_, [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
], )
lowerCamelCase__ : Any = tokenizer.convert_tokens_to_ids(lowerCamelCase_ )
self.assertListEqual(
lowerCamelCase_, [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4], )
lowerCamelCase__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(lowerCamelCase_ )
self.assertListEqual(
lowerCamelCase_, [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
], )
@cached_property
def a__ (self ):
'''simple docstring'''
return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = 'Hello World!'
lowerCamelCase__ : Any = [6_5, 1_8_5_3_6, 2_2_6_0, 1_0_1, 6_6]
self.assertListEqual(lowerCamelCase_, self.big_tokenizer.encode(lowerCamelCase_ ) )
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
# fmt: off
lowerCamelCase__ : Dict = [6_5, 8_7_1, 4_1_9, 3_5_8, 9_4_6, 9_9_1, 2_5_2_1, 4_5_2, 3_5_8, 1_3_5_7, 3_8_7, 7_7_5_1, 3_5_3_6, 1_1_2, 9_8_5, 4_5_6, 1_2_6, 8_6_5, 9_3_8, 5_4_0_0, 5_7_3_4, 4_5_8, 1_3_6_8, 4_6_7, 7_8_6, 2_4_6_2, 5_2_4_6, 1_1_5_9, 6_3_3, 8_6_5, 4_5_1_9, 4_5_7, 5_8_2, 8_5_2, 2_5_5_7, 4_2_7, 9_1_6, 5_0_8, 4_0_5, 3_4_3_2_4, 4_9_7, 3_9_1, 4_0_8, 1_1_3_4_2, 1_2_4_4, 3_8_5, 1_0_0, 9_3_8, 9_8_5, 4_5_6, 5_7_4, 3_6_2, 1_2_5_9_7, 3_2_0_0, 3_1_2_9, 1_1_7_2, 6_6] # noqa: E231
# fmt: on
self.assertListEqual(lowerCamelCase_, self.big_tokenizer.encode(lowerCamelCase_ ) )
@require_torch
@slow
def a__ (self ):
'''simple docstring'''
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
lowerCamelCase__ : Any = list(self.big_tokenizer.get_vocab().keys() )[:1_0]
lowerCamelCase__ : Tuple = ' '.join(lowerCamelCase_ )
lowerCamelCase__ : Any = self.big_tokenizer.encode_plus(lowerCamelCase_, return_tensors='pt', return_token_type_ids=lowerCamelCase_ )
lowerCamelCase__ : Any = self.big_tokenizer.batch_encode_plus(
[sequence + ' ' + sequence], return_tensors='pt', return_token_type_ids=lowerCamelCase_ )
lowerCamelCase__ : int = BigBirdConfig(attention_type='original_full' )
lowerCamelCase__ : str = BigBirdModel(lowerCamelCase_ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**lowerCamelCase_ )
model(**lowerCamelCase_ )
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
lowerCamelCase__ : Dict = tokenizer.decode(tokenizer('Paris is the [MASK].' ).input_ids )
self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]' )
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = {'input_ids': [[6_5, 3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4, 6_6], [6_5, 4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6_5, 4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase_, model_name='google/bigbird-roberta-base', revision='215c99f1600e06f83acce68422f2035b2b5c3510', )
| 696 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
A_ : Optional[int] = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_ = 1_4 ):
'''simple docstring'''
if group not in primes:
raise ValueError('Unsupported Group' )
lowerCamelCase__ : int = primes[group]['prime']
lowerCamelCase__ : Optional[int] = primes[group]['generator']
lowerCamelCase__ : Any = int(hexlify(urandom(3_2 ) ), base=1_6 )
def a__ (self ):
'''simple docstring'''
return hex(self.__private_key )[2:]
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = pow(self.generator, self.__private_key, self.prime )
return hex(lowerCamelCase_ )[2:]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
return (
2 <= key <= self.prime - 2
and pow(lowerCamelCase_, (self.prime - 1) // 2, self.prime ) == 1
)
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = int(lowerCamelCase_, base=1_6 )
if not self.is_valid_public_key(lowerCamelCase_ ):
raise ValueError('Invalid public key' )
lowerCamelCase__ : Tuple = pow(lowerCamelCase_, self.__private_key, self.prime )
return shaaaa(str(lowerCamelCase_ ).encode() ).hexdigest()
@staticmethod
def a__ (lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return (
2 <= remote_public_key_str <= prime - 2
and pow(lowerCamelCase_, (prime - 1) // 2, lowerCamelCase_ ) == 1
)
@staticmethod
def a__ (lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ = 1_4 ):
'''simple docstring'''
lowerCamelCase__ : Dict = int(lowerCamelCase_, base=1_6 )
lowerCamelCase__ : List[Any] = int(lowerCamelCase_, base=1_6 )
lowerCamelCase__ : List[str] = primes[group]['prime']
if not DiffieHellman.is_valid_public_key_static(lowerCamelCase_, lowerCamelCase_ ):
raise ValueError('Invalid public key' )
lowerCamelCase__ : Dict = pow(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
return shaaaa(str(lowerCamelCase_ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 | 1 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : int = XCLIPTextConfig()
# derive patch size from model name
lowerCamelCase__ : Tuple = model_name.find('patch' )
lowerCamelCase__ : str = int(model_name[start_idx + len('patch' ) : start_idx + len('patch' ) + 2] )
lowerCamelCase__ : str = XCLIPVisionConfig(patch_size=_lowerCamelCase , num_frames=_lowerCamelCase )
if "large" in model_name:
lowerCamelCase__ : List[Any] = 768
lowerCamelCase__ : List[Any] = 3072
lowerCamelCase__ : Any = 12
lowerCamelCase__ : str = 1024
lowerCamelCase__ : List[Any] = 4096
lowerCamelCase__ : Optional[Any] = 16
lowerCamelCase__ : List[str] = 24
lowerCamelCase__ : Optional[Any] = 768
lowerCamelCase__ : Union[str, Any] = 3072
if model_name == "xclip-large-patch14-16-frames":
lowerCamelCase__ : Tuple = 336
lowerCamelCase__ : Tuple = XCLIPConfig.from_text_vision_configs(_lowerCamelCase , _lowerCamelCase )
if "large" in model_name:
lowerCamelCase__ : Union[str, Any] = 768
return config
def lowerCamelCase_ ( _lowerCamelCase ):
# text encoder
if name == "token_embedding.weight":
lowerCamelCase__ : str = name.replace('token_embedding.weight' , 'text_model.embeddings.token_embedding.weight' )
if name == "positional_embedding":
lowerCamelCase__ : List[str] = name.replace('positional_embedding' , 'text_model.embeddings.position_embedding.weight' )
if "ln_1" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('ln_1' , 'layer_norm1' )
if "ln_2" in name:
lowerCamelCase__ : Optional[int] = name.replace('ln_2' , 'layer_norm2' )
if "c_fc" in name:
lowerCamelCase__ : Dict = name.replace('c_fc' , 'fc1' )
if "c_proj" in name:
lowerCamelCase__ : Optional[Any] = name.replace('c_proj' , 'fc2' )
if name.startswith('transformer.resblocks' ):
lowerCamelCase__ : Tuple = name.replace('transformer.resblocks' , 'text_model.encoder.layers' )
if "attn.out_proj" in name and "message" not in name:
lowerCamelCase__ : Dict = name.replace('attn.out_proj' , 'self_attn.out_proj' )
if "ln_final" in name:
lowerCamelCase__ : Optional[Any] = name.replace('ln_final' , 'text_model.final_layer_norm' )
# visual encoder
if name == "visual.class_embedding":
lowerCamelCase__ : List[Any] = name.replace('visual.class_embedding' , 'vision_model.embeddings.class_embedding' )
if name == "visual.positional_embedding":
lowerCamelCase__ : Optional[Any] = name.replace('visual.positional_embedding' , 'vision_model.embeddings.position_embedding.weight' )
if name.startswith('visual.transformer.resblocks' ):
lowerCamelCase__ : int = name.replace('visual.transformer.resblocks' , 'vision_model.encoder.layers' )
if "visual.conv1" in name:
lowerCamelCase__ : Dict = name.replace('visual.conv1' , 'vision_model.embeddings.patch_embedding' )
if "visual.ln_pre" in name:
lowerCamelCase__ : Dict = name.replace('visual.ln_pre' , 'vision_model.pre_layernorm' )
if "visual.ln_post" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('visual.ln_post' , 'vision_model.post_layernorm' )
if "visual.proj" in name:
lowerCamelCase__ : str = name.replace('visual.proj' , 'visual_projection.weight' )
if "text_projection" in name:
lowerCamelCase__ : Any = name.replace('text_projection' , 'text_projection.weight' )
# things on top
if "prompts_visual_proj" in name:
lowerCamelCase__ : List[Any] = name.replace('prompts_visual_proj' , 'prompts_visual_projection' )
if "prompts_visual_ln" in name:
lowerCamelCase__ : Union[str, Any] = name.replace('prompts_visual_ln' , 'prompts_visual_layernorm' )
# mit
if name == "mit.positional_embedding":
lowerCamelCase__ : Any = name.replace('positional' , 'position' )
if name.startswith('mit.resblocks' ):
lowerCamelCase__ : Optional[Any] = name.replace('mit.resblocks' , 'mit.encoder.layers' )
# prompts generator
if name.startswith('prompts_generator.norm' ):
lowerCamelCase__ : List[str] = name.replace('prompts_generator.norm' , 'prompts_generator.layernorm' )
return name
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
for key in orig_state_dict.copy().keys():
lowerCamelCase__ : List[Any] = orig_state_dict.pop(_lowerCamelCase )
if "attn.in_proj" in key:
lowerCamelCase__ : Any = key.split('.' )
if key.startswith('visual' ):
lowerCamelCase__ : str = key_split[3]
lowerCamelCase__ : List[Any] = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
lowerCamelCase__ : Union[str, Any] = val[
:dim, :
]
lowerCamelCase__ : str = val[
dim : dim * 2, :
]
lowerCamelCase__ : Tuple = val[
-dim:, :
]
else:
lowerCamelCase__ : int = val[
:dim
]
lowerCamelCase__ : str = val[
dim : dim * 2
]
lowerCamelCase__ : int = val[
-dim:
]
else:
if "weight" in key:
lowerCamelCase__ : Any = val[
:dim, :
]
lowerCamelCase__ : List[str] = val[
dim : dim * 2, :
]
lowerCamelCase__ : Tuple = val[
-dim:, :
]
else:
lowerCamelCase__ : Any = val[:dim]
lowerCamelCase__ : Optional[int] = val[
dim : dim * 2
]
lowerCamelCase__ : Union[str, Any] = val[-dim:]
elif key.startswith('mit' ):
lowerCamelCase__ : Optional[Any] = key_split[2]
lowerCamelCase__ : List[Any] = config.vision_config.mit_hidden_size
if "weight" in key:
lowerCamelCase__ : List[Any] = val[:dim, :]
lowerCamelCase__ : Optional[Any] = val[dim : dim * 2, :]
lowerCamelCase__ : Any = val[-dim:, :]
else:
lowerCamelCase__ : int = val[:dim]
lowerCamelCase__ : Dict = val[dim : dim * 2]
lowerCamelCase__ : Union[str, Any] = val[-dim:]
else:
lowerCamelCase__ : Dict = key_split[2]
lowerCamelCase__ : Any = config.text_config.hidden_size
if "weight" in key:
lowerCamelCase__ : int = val[:dim, :]
lowerCamelCase__ : Optional[int] = val[
dim : dim * 2, :
]
lowerCamelCase__ : Tuple = val[-dim:, :]
else:
lowerCamelCase__ : Optional[Any] = val[:dim]
lowerCamelCase__ : Dict = val[
dim : dim * 2
]
lowerCamelCase__ : List[Any] = val[-dim:]
else:
lowerCamelCase__ : Tuple = rename_key(_lowerCamelCase )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
lowerCamelCase__ : List[Any] = val.T
lowerCamelCase__ : Tuple = val
return orig_state_dict
def lowerCamelCase_ ( _lowerCamelCase ):
if num_frames == 8:
lowerCamelCase__ : int = 'eating_spaghetti_8_frames.npy'
elif num_frames == 16:
lowerCamelCase__ : Optional[int] = 'eating_spaghetti.npy'
elif num_frames == 32:
lowerCamelCase__ : Union[str, Any] = 'eating_spaghetti_32_frames.npy'
lowerCamelCase__ : Tuple = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename=_lowerCamelCase , repo_type='dataset' , )
lowerCamelCase__ : Dict = np.load(_lowerCamelCase )
return list(_lowerCamelCase )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=False ):
lowerCamelCase__ : Tuple = {
# fully supervised kinetics-400 checkpoints
'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
'xclip-base-patch32-16-frames': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
),
'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
'xclip-base-patch16-16-frames': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
),
'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f',
# fully supervised kinetics-600 checkpoints
'xclip-base-patch16-kinetics-600': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
),
'xclip-base-patch16-kinetics-600-16-frames': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
),
'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
# few shot
'xclip-base-patch16-hmdb-2-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
),
'xclip-base-patch16-hmdb-4-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
),
'xclip-base-patch16-hmdb-8-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
),
'xclip-base-patch16-hmdb-16-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
),
'xclip-base-patch16-ucf-2-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
),
'xclip-base-patch16-ucf-4-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
),
'xclip-base-patch16-ucf-8-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
),
'xclip-base-patch16-ucf-16-shot': (
'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
),
# zero shot
'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
}
lowerCamelCase__ : Optional[Any] = model_to_url[model_name]
lowerCamelCase__ : Optional[int] = 8
if "16-frames" in model_name:
lowerCamelCase__ : List[Any] = 16
elif "shot" in model_name:
lowerCamelCase__ : Any = 32
lowerCamelCase__ : Tuple = get_xclip_config(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Optional[Any] = XCLIPModel(_lowerCamelCase )
model.eval()
if "drive" in checkpoint_url:
lowerCamelCase__ : List[Any] = 'pytorch_model.bin'
gdown.cached_download(_lowerCamelCase , _lowerCamelCase , quiet=_lowerCamelCase )
lowerCamelCase__ : int = torch.load(_lowerCamelCase , map_location='cpu' )['model']
else:
lowerCamelCase__ : str = torch.hub.load_state_dict_from_url(_lowerCamelCase )['model']
lowerCamelCase__ : str = convert_state_dict(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = XCLIPModel(_lowerCamelCase )
lowerCamelCase__ , lowerCamelCase__ : List[str] = model.load_state_dict(_lowerCamelCase , strict=_lowerCamelCase )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
lowerCamelCase__ : List[str] = 336 if model_name == 'xclip-large-patch14-16-frames' else 224
lowerCamelCase__ : List[str] = VideoMAEImageProcessor(size=_lowerCamelCase )
lowerCamelCase__ : Dict = CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32' )
lowerCamelCase__ : str = CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32' )
lowerCamelCase__ : List[Any] = XCLIPProcessor(image_processor=_lowerCamelCase , tokenizer=_lowerCamelCase )
lowerCamelCase__ : Any = prepare_video(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = processor(
text=['playing sports', 'eating spaghetti', 'go shopping'] , videos=_lowerCamelCase , return_tensors='pt' , padding=_lowerCamelCase )
print('Shape of pixel values:' , inputs.pixel_values.shape )
with torch.no_grad():
lowerCamelCase__ : int = model(**_lowerCamelCase )
# Verify outputs
lowerCamelCase__ : List[Any] = outputs.logits_per_video
lowerCamelCase__ : Any = logits_per_video.softmax(dim=1 )
print('Probs:' , _lowerCamelCase )
# kinetics-400
if model_name == "xclip-base-patch32":
lowerCamelCase__ : List[Any] = torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
elif model_name == "xclip-base-patch32-16-frames":
lowerCamelCase__ : Optional[int] = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
lowerCamelCase__ : Union[str, Any] = torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
elif model_name == "xclip-base-patch16-16-frames":
lowerCamelCase__ : Union[str, Any] = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
lowerCamelCase__ : str = torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
elif model_name == "xclip-large-patch14-16-frames":
lowerCamelCase__ : List[Any] = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
lowerCamelCase__ : List[str] = torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
lowerCamelCase__ : Tuple = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
lowerCamelCase__ : int = torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
lowerCamelCase__ : Optional[int] = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
lowerCamelCase__ : List[str] = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
lowerCamelCase__ : int = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
lowerCamelCase__ : Any = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
lowerCamelCase__ : List[Any] = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
lowerCamelCase__ : str = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
lowerCamelCase__ : str = torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
lowerCamelCase__ : Any = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
lowerCamelCase__ : List[Any] = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(f'''Model name {model_name} not supported''' )
assert torch.allclose(_lowerCamelCase , _lowerCamelCase , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowerCamelCase )
if push_to_hub:
print('Pushing model, processor and slow tokenizer files to the hub...' )
model.push_to_hub(_lowerCamelCase , organization='nielsr' )
processor.push_to_hub(_lowerCamelCase , organization='nielsr' )
slow_tokenizer.push_to_hub(_lowerCamelCase , organization='nielsr' )
if __name__ == "__main__":
A_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="xclip-base-patch32",
type=str,
help="Name of the model.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
A_ : Any = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 696 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_lowerCamelCase ) * abs(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 696 | 1 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : List[Any] = {
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
# See all BART models at https://huggingface.co/models?filter=bart
}
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = 'bart'
lowerCamelCase__ : Dict = ['past_key_values']
lowerCamelCase__ : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__(self, lowerCamelCase_=5_0_2_6_5, lowerCamelCase_=1_0_2_4, lowerCamelCase_=1_2, lowerCamelCase_=4_0_9_6, lowerCamelCase_=1_6, lowerCamelCase_=1_2, lowerCamelCase_=4_0_9_6, lowerCamelCase_=1_6, lowerCamelCase_=0.0, lowerCamelCase_=0.0, lowerCamelCase_="gelu", lowerCamelCase_=1_0_2_4, lowerCamelCase_=0.1, lowerCamelCase_=0.0, lowerCamelCase_=0.0, lowerCamelCase_=0.02, lowerCamelCase_=0.0, lowerCamelCase_=False, lowerCamelCase_=True, lowerCamelCase_=3, lowerCamelCase_=1, lowerCamelCase_=0, lowerCamelCase_=2, lowerCamelCase_=True, lowerCamelCase_=2, lowerCamelCase_=2, **lowerCamelCase_, ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = vocab_size
lowerCamelCase__ : Dict = max_position_embeddings
lowerCamelCase__ : Dict = d_model
lowerCamelCase__ : Dict = encoder_ffn_dim
lowerCamelCase__ : str = encoder_layers
lowerCamelCase__ : Any = encoder_attention_heads
lowerCamelCase__ : List[str] = decoder_ffn_dim
lowerCamelCase__ : int = decoder_layers
lowerCamelCase__ : str = decoder_attention_heads
lowerCamelCase__ : Optional[Any] = dropout
lowerCamelCase__ : Union[str, Any] = attention_dropout
lowerCamelCase__ : Any = activation_dropout
lowerCamelCase__ : List[str] = activation_function
lowerCamelCase__ : List[str] = init_std
lowerCamelCase__ : List[Any] = encoder_layerdrop
lowerCamelCase__ : Tuple = decoder_layerdrop
lowerCamelCase__ : int = classifier_dropout
lowerCamelCase__ : str = use_cache
lowerCamelCase__ : Optional[Any] = encoder_layers
lowerCamelCase__ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=lowerCamelCase_, pad_token_id=lowerCamelCase_, bos_token_id=lowerCamelCase_, eos_token_id=lowerCamelCase_, is_encoder_decoder=lowerCamelCase_, decoder_start_token_id=lowerCamelCase_, forced_eos_token_id=lowerCamelCase_, **lowerCamelCase_, )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated', lowerCamelCase_ ):
lowerCamelCase__ : str = self.bos_token_id
warnings.warn(
f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
'The config can simply be saved and uploaded again to be fixed.' )
class a_ ( snake_case_ ):
'''simple docstring'''
@property
def a__ (self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
lowerCamelCase__ : List[Any] = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
] )
if self.use_past:
lowerCamelCase__ : Optional[int] = {0: 'batch'}
lowerCamelCase__ : Optional[Any] = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
lowerCamelCase__ : Optional[Any] = {0: 'batch', 1: 'decoder_sequence'}
lowerCamelCase__ : List[str] = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase_, direction='inputs' )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowerCamelCase__ : Dict = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
] )
if self.use_past:
lowerCamelCase__ , lowerCamelCase__ : Dict = self.num_layers
for i in range(lowerCamelCase_ ):
lowerCamelCase__ : Tuple = {0: 'batch', 2: 'past_sequence + sequence'}
lowerCamelCase__ : Optional[Any] = {0: 'batch', 2: 'past_sequence + sequence'}
else:
lowerCamelCase__ : Tuple = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
] )
return common_inputs
@property
def a__ (self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
lowerCamelCase__ : List[str] = super().outputs
else:
lowerCamelCase__ : Optional[int] = super(lowerCamelCase_, self ).outputs
if self.use_past:
lowerCamelCase__ , lowerCamelCase__ : int = self.num_layers
for i in range(lowerCamelCase_ ):
lowerCamelCase__ : List[str] = {0: 'batch', 2: 'past_sequence + sequence'}
lowerCamelCase__ : Any = {0: 'batch', 2: 'past_sequence + sequence'}
return common_outputs
def a__ (self, lowerCamelCase_, lowerCamelCase_ = -1, lowerCamelCase_ = -1, lowerCamelCase_ = False, lowerCamelCase_ = None, ):
'''simple docstring'''
lowerCamelCase__ : Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
# Generate decoder inputs
lowerCamelCase__ : str = seq_length if not self.use_past else 1
lowerCamelCase__ : List[str] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Any = {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
lowerCamelCase__ : int = dict(**lowerCamelCase_, **lowerCamelCase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = common_inputs['input_ids'].shape
lowerCamelCase__ : Optional[Any] = common_inputs['decoder_input_ids'].shape[1]
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = self.num_attention_heads
lowerCamelCase__ : str = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowerCamelCase__ : Any = decoder_seq_length + 3
lowerCamelCase__ : int = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowerCamelCase__ : Optional[Any] = torch.cat(
[common_inputs['decoder_attention_mask'], torch.ones(lowerCamelCase_, lowerCamelCase_ )], dim=1 )
lowerCamelCase__ : int = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = self.num_layers
lowerCamelCase__ : Any = min(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : int = max(lowerCamelCase_, lowerCamelCase_ ) - min_num_layers
lowerCamelCase__ : Optional[int] = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
for _ in range(lowerCamelCase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowerCamelCase_ ),
torch.zeros(lowerCamelCase_ ),
torch.zeros(lowerCamelCase_ ),
torch.zeros(lowerCamelCase_ ),
) )
# TODO: test this.
lowerCamelCase__ : str = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
for _ in range(lowerCamelCase_, lowerCamelCase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase_ ), torch.zeros(lowerCamelCase_ )) )
return common_inputs
def a__ (self, lowerCamelCase_, lowerCamelCase_ = -1, lowerCamelCase_ = -1, lowerCamelCase_ = False, lowerCamelCase_ = None, ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ : Any = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
lowerCamelCase__ : Dict = seqlen + 2
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = self.num_layers
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.num_attention_heads
lowerCamelCase__ : int = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowerCamelCase__ : List[str] = common_inputs['attention_mask'].dtype
lowerCamelCase__ : List[str] = torch.cat(
[common_inputs['attention_mask'], torch.ones(lowerCamelCase_, lowerCamelCase_, dtype=lowerCamelCase_ )], dim=1 )
lowerCamelCase__ : List[Any] = [
(torch.zeros(lowerCamelCase_ ), torch.zeros(lowerCamelCase_ )) for _ in range(lowerCamelCase_ )
]
return common_inputs
def a__ (self, lowerCamelCase_, lowerCamelCase_ = -1, lowerCamelCase_ = -1, lowerCamelCase_ = False, lowerCamelCase_ = None, ):
'''simple docstring'''
lowerCamelCase__ : Dict = compute_effective_axis_dimension(
lowerCamelCase_, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowerCamelCase__ : List[str] = tokenizer.num_special_tokens_to_add(lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = compute_effective_axis_dimension(
lowerCamelCase_, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=lowerCamelCase_ )
# Generate dummy inputs according to compute batch and sequence
lowerCamelCase__ : List[Any] = [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowerCamelCase__ : Tuple = dict(tokenizer(lowerCamelCase_, return_tensors=lowerCamelCase_ ) )
return common_inputs
def a__ (self, lowerCamelCase_, lowerCamelCase_ = -1, lowerCamelCase_ = -1, lowerCamelCase_ = False, lowerCamelCase_ = None, ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
lowerCamelCase__ : List[Any] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowerCamelCase_, batch_size=lowerCamelCase_, seq_length=lowerCamelCase_, is_pair=lowerCamelCase_, framework=lowerCamelCase_ )
elif self.task == "causal-lm":
lowerCamelCase__ : List[str] = self._generate_dummy_inputs_for_causal_lm(
lowerCamelCase_, batch_size=lowerCamelCase_, seq_length=lowerCamelCase_, is_pair=lowerCamelCase_, framework=lowerCamelCase_ )
else:
lowerCamelCase__ : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCamelCase_, batch_size=lowerCamelCase_, seq_length=lowerCamelCase_, is_pair=lowerCamelCase_, framework=lowerCamelCase_ )
return common_inputs
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
lowerCamelCase__ : str = super()._flatten_past_key_values_(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
else:
lowerCamelCase__ : Any = super(lowerCamelCase_, self )._flatten_past_key_values_(
lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
| 696 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
A_ : int = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 1_28,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
@is_staging_test
class a_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def a__ (cls ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def a__ (cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id='test-dynamic-config' )
except HTTPError:
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('test-config', use_auth_token=self._token )
lowerCamelCase__ : Optional[int] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase_, repo_id='test-config', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : List[str] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = BertConfig(
vocab_size=9_9, hidden_size=3_2, num_hidden_layers=5, num_attention_heads=4, intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org', use_auth_token=self._token )
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token, repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase_, repo_id='valid_org/test-config-org', push_to_hub=lowerCamelCase_, use_auth_token=self._token )
lowerCamelCase__ : str = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_, getattr(lowerCamelCase_, lowerCamelCase_ ) )
def a__ (self ):
'''simple docstring'''
CustomConfig.register_for_auto_class()
lowerCamelCase__ : Optional[int] = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config', use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map, {'AutoConfig': 'custom_configuration.CustomConfig'} )
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config''', trust_remote_code=lowerCamelCase_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__, 'CustomConfig' )
self.assertEqual(new_config.attribute, 4_2 )
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
lowerCamelCase__ : Tuple = c.n_embd + 1 # int
lowerCamelCase__ : Union[str, Any] = c.resid_pdrop + 1.0 # float
lowerCamelCase__ : List[Any] = not c.scale_attn_weights # bool
lowerCamelCase__ : List[Any] = c.summary_type + 'foo' # str
c.update_from_string(
f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(lowerCamelCase_, c.n_embd, 'mismatch for key: n_embd' )
self.assertEqual(lowerCamelCase_, c.resid_pdrop, 'mismatch for key: resid_pdrop' )
self.assertEqual(lowerCamelCase_, c.scale_attn_weights, 'mismatch for key: scale_attn_weights' )
self.assertEqual(lowerCamelCase_, c.summary_type, 'mismatch for key: summary_type' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = PretrainedConfig()
lowerCamelCase__ : Optional[Any] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowerCamelCase_, ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
lowerCamelCase__ : Any = [key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase_, lowerCamelCase_ )]
if len(lowerCamelCase_ ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
f''' {', '.join(lowerCamelCase_ )}.''' )
def a__ (self ):
'''simple docstring'''
with self.assertRaises(lowerCamelCase_ ):
# config is in subfolder, the following should not work without specifying the subfolder
lowerCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
lowerCamelCase__ : int = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder', subfolder='bert' )
self.assertIsNotNone(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = mock.Mock()
lowerCamelCase__ : List[str] = 5_0_0
lowerCamelCase__ : Any = {}
lowerCamelCase__ : int = HTTPError
lowerCamelCase__ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
lowerCamelCase__ : Any = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request', return_value=lowerCamelCase_ ) as mock_head:
lowerCamelCase__ : List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = AutoConfig.from_pretrained('bert-base-cased' )
lowerCamelCase__ : str = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = 2
json.dump(configuration.to_dict(), open(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
lowerCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
lowerCamelCase__ : str = ['config.42.0.0.json']
lowerCamelCase__ : Union[str, Any] = 7_6_8
configuration.save_pretrained(lowerCamelCase_ )
shutil.move(os.path.join(lowerCamelCase_, 'config.4.0.0.json' ), os.path.join(lowerCamelCase_, 'config.42.0.0.json' ) )
lowerCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 7_6_8 )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
lowerCamelCase__ : Optional[int] = 'v4.0.0'
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
lowerCamelCase_, return_unused_kwargs=lowerCamelCase_ )
self.assertEqual(new_configuration.hidden_size, 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowerCamelCase_, {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
lowerCamelCase__ : Dict = 'v3.0.0'
lowerCamelCase__ : List[str] = old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase_ )
self.assertEqual(old_configuration.hidden_size, 7_6_8 )
| 696 | 1 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
lowerCamelCase__ : str = FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase )
lowerCamelCase__ : Any = checkpoints.load_tax_checkpoint(_lowerCamelCase )
lowerCamelCase__ : Dict = 'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']
if config.model_type == "t5":
lowerCamelCase__ : Union[str, Any] = 'SelfAttention'
if config.model_type == "longt5" and config.encoder_attention_type == "local":
lowerCamelCase__ : Optional[int] = 'LocalSelfAttention'
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCamelCase__ : str = 'TransientGlobalSelfAttention'
else:
raise ValueError(
'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
' attribute with a value from [\'local\', \'transient-global].' )
# Encoder
for layer_index in range(config.num_layers ):
lowerCamelCase__ : Any = f'''layers_{str(_lowerCamelCase )}'''
# Self-Attention
lowerCamelCase__ : Tuple = tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
lowerCamelCase__ : str = tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
lowerCamelCase__ : int = tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
lowerCamelCase__ : Union[str, Any] = tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCamelCase__ : Union[str, Any] = tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']
# Layer Normalization
lowerCamelCase__ : List[str] = tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']
if split_mlp_wi:
lowerCamelCase__ : Tuple = tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
lowerCamelCase__ : List[Any] = tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
else:
lowerCamelCase__ : Tuple = tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']
lowerCamelCase__ : Any = tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
lowerCamelCase__ : Dict = tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
lowerCamelCase__ : Union[str, Any] = flax_model.params['encoder']['block'][str(_lowerCamelCase )]['layer']
lowerCamelCase__ : Optional[int] = tax_attention_key
lowerCamelCase__ : Dict = tax_attention_out
lowerCamelCase__ : Union[str, Any] = tax_attention_query
lowerCamelCase__ : Dict = tax_attention_value
lowerCamelCase__ : str = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCamelCase__ : Optional[int] = tax_global_layer_norm
if split_mlp_wi:
lowerCamelCase__ : Tuple = tax_mlp_wi_a
lowerCamelCase__ : Optional[Any] = tax_mlp_wi_a
else:
lowerCamelCase__ : str = tax_mlp_wi
lowerCamelCase__ : str = tax_mlp_wo
lowerCamelCase__ : Tuple = tax_mlp_layer_norm
lowerCamelCase__ : int = flax_model_encoder_layer_block
# Only for layer 0:
lowerCamelCase__ : Optional[int] = tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
lowerCamelCase__ : Any = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCamelCase__ : Optional[Any] = tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
lowerCamelCase__ : str = tax_encoder_global_rel_embedding
# Assigning
lowerCamelCase__ : str = tax_model['target']['encoder']['encoder_norm']['scale']
lowerCamelCase__ : str = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
lowerCamelCase__ : Optional[int] = f'''layers_{str(_lowerCamelCase )}'''
# Self-Attention
lowerCamelCase__ : Dict = tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
lowerCamelCase__ : Dict = tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
lowerCamelCase__ : List[Any] = tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
lowerCamelCase__ : str = tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']
# Layer Normalization
lowerCamelCase__ : Union[str, Any] = tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
'scale'
]
# Encoder-Decoder-Attention
lowerCamelCase__ : Tuple = tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
lowerCamelCase__ : int = tax_enc_dec_attention_module['key']['kernel']
lowerCamelCase__ : Dict = tax_enc_dec_attention_module['out']['kernel']
lowerCamelCase__ : int = tax_enc_dec_attention_module['query']['kernel']
lowerCamelCase__ : int = tax_enc_dec_attention_module['value']['kernel']
# Layer Normalization
lowerCamelCase__ : Dict = tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']
# MLP
if split_mlp_wi:
lowerCamelCase__ : Dict = tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
lowerCamelCase__ : int = tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
else:
lowerCamelCase__ : str = tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']
lowerCamelCase__ : Any = tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
lowerCamelCase__ : Optional[int] = tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
lowerCamelCase__ : Union[str, Any] = flax_model.params['decoder']['block'][str(_lowerCamelCase )]['layer']
lowerCamelCase__ : List[str] = tax_attention_key
lowerCamelCase__ : int = tax_attention_out
lowerCamelCase__ : Optional[Any] = tax_attention_query
lowerCamelCase__ : Union[str, Any] = tax_attention_value
lowerCamelCase__ : Union[str, Any] = tax_pre_attention_layer_norm
lowerCamelCase__ : Optional[int] = tax_enc_dec_attention_key
lowerCamelCase__ : str = tax_enc_dec_attention_out
lowerCamelCase__ : Union[str, Any] = tax_enc_dec_attention_query
lowerCamelCase__ : Dict = tax_enc_dec_attention_value
lowerCamelCase__ : List[str] = tax_cross_layer_norm
if split_mlp_wi:
lowerCamelCase__ : Optional[int] = tax_mlp_wi_a
lowerCamelCase__ : Union[str, Any] = tax_mlp_wi_a
else:
lowerCamelCase__ : Tuple = tax_mlp_wi
lowerCamelCase__ : Dict = tax_mlp_wo
lowerCamelCase__ : Any = txa_mlp_layer_norm
lowerCamelCase__ : str = flax_model_decoder_layer_block
# Decoder Normalization
lowerCamelCase__ : str = tax_model['target']['decoder']['decoder_norm']['scale']
lowerCamelCase__ : Dict = txa_decoder_norm
# Only for layer 0:
lowerCamelCase__ : List[str] = tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
lowerCamelCase__ : Dict = tax_decoder_rel_embedding
# Token Embeddings
lowerCamelCase__ : List[Any] = tax_model['target']['token_embedder']['embedding']
lowerCamelCase__ : Optional[Any] = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
lowerCamelCase__ : List[Any] = tax_model['target']['decoder']['logits_dense']['kernel']
flax_model.save_pretrained(_lowerCamelCase )
print('T5X Model was sucessfully converted!' )
if __name__ == "__main__":
A_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
)
parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
parser.add_argument(
"--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
)
A_ : int = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 696 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
super().__init__()
lowerCamelCase__ : Dict = value_function
lowerCamelCase__ : int = unet
lowerCamelCase__ : Union[str, Any] = scheduler
lowerCamelCase__ : int = env
lowerCamelCase__ : List[Any] = env.get_dataset()
lowerCamelCase__ : Dict = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
lowerCamelCase__ : Optional[int] = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Tuple = self.data[key].std()
except: # noqa: E722
pass
lowerCamelCase__ : Optional[Any] = env.observation_space.shape[0]
lowerCamelCase__ : List[str] = env.action_space.shape[0]
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
if type(lowerCamelCase_ ) is dict:
return {k: self.to_torch(lowerCamelCase_ ) for k, v in x_in.items()}
elif torch.is_tensor(lowerCamelCase_ ):
return x_in.to(self.unet.device )
return torch.tensor(lowerCamelCase_, device=self.unet.device )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
for key, val in cond.items():
lowerCamelCase__ : Optional[Any] = val.clone()
return x_in
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = x.shape[0]
lowerCamelCase__ : Tuple = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowerCamelCase__ : Dict = torch.full((batch_size,), lowerCamelCase_, device=self.unet.device, dtype=torch.long )
for _ in range(lowerCamelCase_ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowerCamelCase__ : str = self.value_function(x.permute(0, 2, 1 ), lowerCamelCase_ ).sample
lowerCamelCase__ : Union[str, Any] = torch.autograd.grad([y.sum()], [x] )[0]
lowerCamelCase__ : Optional[int] = self.scheduler._get_variance(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = torch.exp(0.5 * posterior_variance )
lowerCamelCase__ : Tuple = model_std * grad
lowerCamelCase__ : str = 0
lowerCamelCase__ : Dict = x.detach()
lowerCamelCase__ : Dict = x + scale * grad
lowerCamelCase__ : Optional[int] = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : Tuple = self.unet(x.permute(0, 2, 1 ), lowerCamelCase_ ).sample.permute(0, 2, 1 )
# TODO: verify deprecation of this kwarg
lowerCamelCase__ : Optional[Any] = self.scheduler.step(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, predict_epsilon=lowerCamelCase_ )['prev_sample']
# apply conditions to the trajectory (set the initial state)
lowerCamelCase__ : Any = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : List[str] = self.to_torch(lowerCamelCase_ )
return x, y
def __call__(self, lowerCamelCase_, lowerCamelCase_=6_4, lowerCamelCase_=3_2, lowerCamelCase_=2, lowerCamelCase_=0.1 ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.normalize(lowerCamelCase_, 'observations' )
lowerCamelCase__ : List[str] = obs[None].repeat(lowerCamelCase_, axis=0 )
lowerCamelCase__ : str = {0: self.to_torch(lowerCamelCase_ )}
lowerCamelCase__ : Optional[Any] = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowerCamelCase__ : List[Any] = randn_tensor(lowerCamelCase_, device=self.unet.device )
lowerCamelCase__ : int = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : List[str] = self.to_torch(lowerCamelCase_ )
# run the diffusion process
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.run_diffusion(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
# sort output trajectories by value
lowerCamelCase__ : Union[str, Any] = y.argsort(0, descending=lowerCamelCase_ ).squeeze()
lowerCamelCase__ : List[str] = x[sorted_idx]
lowerCamelCase__ : Optional[Any] = sorted_values[:, :, : self.action_dim]
lowerCamelCase__ : Union[str, Any] = actions.detach().cpu().numpy()
lowerCamelCase__ : Union[str, Any] = self.de_normalize(lowerCamelCase_, key='actions' )
# select the action with the highest value
if y is not None:
lowerCamelCase__ : str = 0
else:
# if we didn't run value guiding, select a random action
lowerCamelCase__ : Optional[Any] = np.random.randint(0, lowerCamelCase_ )
lowerCamelCase__ : Tuple = denorm_actions[selected_index, 0]
return denorm_actions
| 696 | 1 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase=0.999 , _lowerCamelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(_lowerCamelCase ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_lowerCamelCase ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
lowerCamelCase__ : Tuple = []
for i in range(_lowerCamelCase ):
lowerCamelCase__ : Any = i / num_diffusion_timesteps
lowerCamelCase__ : Optional[Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_lowerCamelCase ) / alpha_bar_fn(_lowerCamelCase ) , _lowerCamelCase ) )
return torch.tensor(_lowerCamelCase , dtype=torch.floataa )
class a_ ( snake_case_ , snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = [e.name for e in KarrasDiffusionSchedulers]
lowerCamelCase__ : int = 2
@register_to_config
def __init__(self, lowerCamelCase_ = 1_0_0_0, lowerCamelCase_ = 0.00_085, lowerCamelCase_ = 0.012, lowerCamelCase_ = "linear", lowerCamelCase_ = None, lowerCamelCase_ = "epsilon", lowerCamelCase_ = "linspace", lowerCamelCase_ = 0, ):
'''simple docstring'''
if trained_betas is not None:
lowerCamelCase__ : Dict = torch.tensor(lowerCamelCase_, dtype=torch.floataa )
elif beta_schedule == "linear":
lowerCamelCase__ : Any = torch.linspace(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowerCamelCase__ : Any = (
torch.linspace(beta_start**0.5, beta_end**0.5, lowerCamelCase_, dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowerCamelCase__ : int = betas_for_alpha_bar(lowerCamelCase_ )
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )
lowerCamelCase__ : Optional[int] = 1.0 - self.betas
lowerCamelCase__ : Optional[int] = torch.cumprod(self.alphas, dim=0 )
# set all values
self.set_timesteps(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_=None ):
'''simple docstring'''
if schedule_timesteps is None:
lowerCamelCase__ : Dict = self.timesteps
lowerCamelCase__ : List[Any] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
lowerCamelCase__ : Optional[Any] = 1 if len(lowerCamelCase_ ) > 1 else 0
else:
lowerCamelCase__ : List[str] = timestep.cpu().item() if torch.is_tensor(lowerCamelCase_ ) else timestep
lowerCamelCase__ : List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def a__ (self ):
'''simple docstring'''
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def a__ (self, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
lowerCamelCase__ : List[str] = self.index_for_timestep(lowerCamelCase_ )
if self.state_in_first_order:
lowerCamelCase__ : List[str] = self.sigmas[step_index]
else:
lowerCamelCase__ : Union[str, Any] = self.sigmas_interpol[step_index]
lowerCamelCase__ : Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def a__ (self, lowerCamelCase_, lowerCamelCase_ = None, lowerCamelCase_ = None, ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = num_inference_steps
lowerCamelCase__ : Any = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
lowerCamelCase__ : str = np.linspace(0, num_train_timesteps - 1, lowerCamelCase_, dtype=lowerCamelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
lowerCamelCase__ : int = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCamelCase__ : List[Any] = (np.arange(0, lowerCamelCase_ ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
lowerCamelCase__ : Optional[int] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCamelCase__ : Dict = (np.arange(lowerCamelCase_, 0, -step_ratio )).round().copy().astype(lowerCamelCase_ )
timesteps -= 1
else:
raise ValueError(
f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )
lowerCamelCase__ : str = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
lowerCamelCase__ : List[str] = torch.from_numpy(np.log(lowerCamelCase_ ) ).to(lowerCamelCase_ )
lowerCamelCase__ : int = np.interp(lowerCamelCase_, np.arange(0, len(lowerCamelCase_ ) ), lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
lowerCamelCase__ : Union[str, Any] = torch.from_numpy(lowerCamelCase_ ).to(device=lowerCamelCase_ )
# interpolate sigmas
lowerCamelCase__ : Any = sigmas.log().lerp(sigmas.roll(1 ).log(), 0.5 ).exp()
lowerCamelCase__ : Optional[Any] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
lowerCamelCase__ : Union[str, Any] = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(lowerCamelCase_ ).startswith('mps' ):
# mps does not support float64
lowerCamelCase__ : List[Any] = torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_, dtype=torch.floataa )
else:
lowerCamelCase__ : List[Any] = torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )
# interpolate timesteps
lowerCamelCase__ : Any = self.sigma_to_t(lowerCamelCase_ ).to(lowerCamelCase_, dtype=timesteps.dtype )
lowerCamelCase__ : Dict = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]), dim=-1 ).flatten()
lowerCamelCase__ : Optional[Any] = torch.cat([timesteps[:1], interleaved_timesteps] )
lowerCamelCase__ : Tuple = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
lowerCamelCase__ : Optional[Any] = defaultdict(lowerCamelCase_ )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = sigma.log()
# get distribution
lowerCamelCase__ : Optional[Any] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
lowerCamelCase__ : List[Any] = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
lowerCamelCase__ : int = low_idx + 1
lowerCamelCase__ : Tuple = self.log_sigmas[low_idx]
lowerCamelCase__ : List[Any] = self.log_sigmas[high_idx]
# interpolate sigmas
lowerCamelCase__ : Any = (low - log_sigma) / (low - high)
lowerCamelCase__ : Optional[Any] = w.clamp(0, 1 )
# transform interpolation to time range
lowerCamelCase__ : Optional[Any] = (1 - w) * low_idx + w * high_idx
lowerCamelCase__ : Tuple = t.view(sigma.shape )
return t
@property
def a__ (self ):
'''simple docstring'''
return self.sample is None
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ = True, ):
'''simple docstring'''
lowerCamelCase__ : int = self.index_for_timestep(lowerCamelCase_ )
# advance index counter by 1
lowerCamelCase__ : List[Any] = timestep.cpu().item() if torch.is_tensor(lowerCamelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
lowerCamelCase__ : Union[str, Any] = self.sigmas[step_index]
lowerCamelCase__ : Union[str, Any] = self.sigmas_interpol[step_index + 1]
lowerCamelCase__ : str = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
lowerCamelCase__ : Union[str, Any] = self.sigmas[step_index - 1]
lowerCamelCase__ : List[str] = self.sigmas_interpol[step_index]
lowerCamelCase__ : Optional[Any] = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
lowerCamelCase__ : Optional[int] = 0
lowerCamelCase__ : Tuple = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
lowerCamelCase__ : str = sigma_hat if self.state_in_first_order else sigma_interpol
lowerCamelCase__ : Optional[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
lowerCamelCase__ : Any = sigma_hat if self.state_in_first_order else sigma_interpol
lowerCamelCase__ : List[Any] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError('prediction_type not implemented yet: sample' )
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
lowerCamelCase__ : Any = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
lowerCamelCase__ : str = sigma_interpol - sigma_hat
# store for 2nd order step
lowerCamelCase__ : str = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
lowerCamelCase__ : Tuple = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
lowerCamelCase__ : Union[str, Any] = sigma_next - sigma_hat
lowerCamelCase__ : Dict = self.sample
lowerCamelCase__ : Optional[Any] = None
lowerCamelCase__ : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_ ):
# mps does not support float64
lowerCamelCase__ : Tuple = self.timesteps.to(original_samples.device, dtype=torch.floataa )
lowerCamelCase__ : Union[str, Any] = timesteps.to(original_samples.device, dtype=torch.floataa )
else:
lowerCamelCase__ : str = self.timesteps.to(original_samples.device )
lowerCamelCase__ : Union[str, Any] = timesteps.to(original_samples.device )
lowerCamelCase__ : Dict = [self.index_for_timestep(lowerCamelCase_, lowerCamelCase_ ) for t in timesteps]
lowerCamelCase__ : Optional[int] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
lowerCamelCase__ : Optional[int] = sigma.unsqueeze(-1 )
lowerCamelCase__ : Tuple = original_samples + noise * sigma
return noisy_samples
def __len__(self ):
'''simple docstring'''
return self.config.num_train_timesteps
| 696 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ , lowerCamelCase__ : List[str] = analyze_text(_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = list(' ' + ascii_lowercase )
# what is our total sum of probabilities.
lowerCamelCase__ : List[Any] = sum(single_char_strings.values() )
# one length string
lowerCamelCase__ : str = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
lowerCamelCase__ : Tuple = single_char_strings[ch]
lowerCamelCase__ : Union[str, Any] = my_str / all_sum
my_fir_sum += prob * math.loga(_lowerCamelCase ) # entropy formula.
# print entropy
print(f'''{round(-1 * my_fir_sum ):.1f}''' )
# two len string
lowerCamelCase__ : Dict = sum(two_char_strings.values() )
lowerCamelCase__ : str = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
lowerCamelCase__ : int = cha + cha
if sequence in two_char_strings:
lowerCamelCase__ : int = two_char_strings[sequence]
lowerCamelCase__ : Tuple = int(_lowerCamelCase ) / all_sum
my_sec_sum += prob * math.loga(_lowerCamelCase )
# print second entropy
print(f'''{round(-1 * my_sec_sum ):.1f}''' )
# print the difference between them
print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}''' )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : List[str] = Counter() # type: ignore
lowerCamelCase__ : List[Any] = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(_lowerCamelCase ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def lowerCamelCase_ ( ):
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 696 | 1 |
"""simple docstring"""
import math
def lowerCamelCase_ ( _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowerCamelCase )
if number < 1:
lowerCamelCase__ : Optional[int] = f'''Input value of [number={number}] must be > 0'''
raise ValueError(_lowerCamelCase )
elif number == 1:
return 3
elif number == 2:
return 5
else:
lowerCamelCase__ : Any = int(math.log(number // 3 , 2 ) ) + 2
lowerCamelCase__ : List[Any] = [3, 5]
lowerCamelCase__ : List[Any] = 2
lowerCamelCase__ : List[Any] = 3
for block in range(1 , _lowerCamelCase ):
for _ in range(_lowerCamelCase ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
A_ : Dict = 0
try:
A_ : Any = proth(number)
except ValueError:
print(f"ValueError: there is no {number}th Proth number")
continue
print(f"The {number}th Proth number: {value}")
| 696 |
"""simple docstring"""
import os
def lowerCamelCase_ ( ):
with open(os.path.dirname(_lowerCamelCase ) + '/p022_names.txt' ) as file:
lowerCamelCase__ : Union[str, Any] = str(file.readlines()[0] )
lowerCamelCase__ : int = names.replace('"' , '' ).split(',' )
names.sort()
lowerCamelCase__ : Tuple = 0
lowerCamelCase__ : str = 0
for i, name in enumerate(_lowerCamelCase ):
for letter in name:
name_score += ord(_lowerCamelCase ) - 64
total_score += (i + 1) * name_score
lowerCamelCase__ : Dict = 0
return total_score
if __name__ == "__main__":
print(solution())
| 696 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=False, lowerCamelCase_=True, lowerCamelCase_=False, lowerCamelCase_=False, lowerCamelCase_=1_9, lowerCamelCase_=3_2, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Tuple = parent
lowerCamelCase__ : List[str] = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : str = is_training
lowerCamelCase__ : str = use_input_mask
lowerCamelCase__ : Dict = use_token_type_ids
lowerCamelCase__ : Optional[Any] = use_labels
lowerCamelCase__ : Any = vocab_size
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : List[Any] = num_hidden_layers
lowerCamelCase__ : str = num_attention_heads
lowerCamelCase__ : Any = intermediate_size
lowerCamelCase__ : str = hidden_act
lowerCamelCase__ : Any = hidden_dropout_prob
lowerCamelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCamelCase__ : Union[str, Any] = max_position_embeddings
lowerCamelCase__ : str = type_vocab_size
lowerCamelCase__ : Dict = type_sequence_label_size
lowerCamelCase__ : List[str] = initializer_range
lowerCamelCase__ : Dict = num_labels
lowerCamelCase__ : str = num_choices
lowerCamelCase__ : Any = scope
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : List[Any] = None
if self.use_input_mask:
lowerCamelCase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : int = None
if self.use_labels:
lowerCamelCase__ : List[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : Any = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : Any = ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase__ : Tuple = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = EsmConfig(
vocab_size=3_3, hidden_size=self.hidden_size, pad_token_id=1, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, is_folding_model=lowerCamelCase_, esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}, )
return config
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = EsmForProteinFolding(config=lowerCamelCase_ ).float()
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Any = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
lowerCamelCase__ : Any = model(lowerCamelCase_ )
lowerCamelCase__ : str = model(lowerCamelCase_ )
self.parent.assertEqual(result.positions.shape, (8, self.batch_size, self.seq_length, 1_4, 3) )
self.parent.assertEqual(result.angles.shape, (8, self.batch_size, self.seq_length, 7, 2) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : str = config_and_inputs
lowerCamelCase__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = False
lowerCamelCase__ : List[str] = (EsmForProteinFolding,) if is_torch_available() else ()
lowerCamelCase__ : Union[str, Any] = ()
lowerCamelCase__ : Any = {} if is_torch_available() else {}
lowerCamelCase__ : str = False
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = EsmFoldModelTester(self )
lowerCamelCase__ : Optional[Any] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=3_7 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
@unittest.skip('Does not support attention outputs' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('Esm does not support embedding resizing' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support passing input embeds!' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support head pruning.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not output hidden states in the normal way.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMfold does not output hidden states in the normal way.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold only has one output format.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold does not support input chunking.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('ESMFold doesn\'t support data parallel.' )
def a__ (self ):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def a__ (self ):
'''simple docstring'''
pass
@require_torch
class a_ ( snake_case_ ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = EsmForProteinFolding.from_pretrained('facebook/esmfold_v1' ).float()
model.eval()
lowerCamelCase__ : Optional[Any] = torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
lowerCamelCase__ : Dict = model(lowerCamelCase_ )['positions']
lowerCamelCase__ : Optional[int] = torch.tensor([2.5_828, 0.7_993, -10.9_334], dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0], lowerCamelCase_, atol=1e-4 ) )
| 696 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : int = 'Speech2TextFeatureExtractor'
lowerCamelCase__ : Dict = 'Speech2TextTokenizer'
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
super().__init__(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : List[str] = self.feature_extractor
lowerCamelCase__ : List[Any] = False
def __call__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*lowerCamelCase_, **lowerCamelCase_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
lowerCamelCase__ : Optional[int] = kwargs.pop('raw_speech' )
else:
lowerCamelCase__ : int = kwargs.pop('audio', lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = kwargs.pop('sampling_rate', lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = kwargs.pop('text', lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
lowerCamelCase__ : List[str] = args[0]
lowerCamelCase__ : Any = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
lowerCamelCase__ : Union[str, Any] = self.feature_extractor(lowerCamelCase_, *lowerCamelCase_, sampling_rate=lowerCamelCase_, **lowerCamelCase_ )
if text is not None:
lowerCamelCase__ : List[Any] = self.tokenizer(lowerCamelCase_, **lowerCamelCase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCamelCase__ : Tuple = encodings['input_ids']
return inputs
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase_, **lowerCamelCase_ )
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase_, **lowerCamelCase_ )
@contextmanager
def a__ (self ):
'''simple docstring'''
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
lowerCamelCase__ : int = True
lowerCamelCase__ : List[Any] = self.tokenizer
yield
lowerCamelCase__ : Optional[int] = self.feature_extractor
lowerCamelCase__ : List[Any] = False
| 696 | 1 |
"""simple docstring"""
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : List[Any] = k_size // 2
lowerCamelCase__ , lowerCamelCase__ : Tuple = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
lowerCamelCase__ : Optional[Any] = 1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase ) + square(_lowerCamelCase )) / (2 * square(_lowerCamelCase )) )
return g
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = image.shape[0], image.shape[1]
# dst image height and width
lowerCamelCase__ : int = height - k_size + 1
lowerCamelCase__ : Dict = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
lowerCamelCase__ : str = zeros((dst_height * dst_width, k_size * k_size) )
lowerCamelCase__ : Optional[Any] = 0
for i, j in product(range(_lowerCamelCase ) , range(_lowerCamelCase ) ):
lowerCamelCase__ : Union[str, Any] = ravel(image[i : i + k_size, j : j + k_size] )
lowerCamelCase__ : int = window
row += 1
# turn the kernel into shape(k*k, 1)
lowerCamelCase__ : Optional[int] = gen_gaussian_kernel(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = ravel(_lowerCamelCase )
# reshape and get the dst image
lowerCamelCase__ : List[str] = dot(_lowerCamelCase , _lowerCamelCase ).reshape(_lowerCamelCase , _lowerCamelCase ).astype(_lowerCamelCase )
return dst
if __name__ == "__main__":
# read original image
A_ : Union[str, Any] = imread(r"../image_data/lena.jpg")
# turn image in gray scale value
A_ : List[Any] = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
A_ : Tuple = gaussian_filter(gray, 3, sigma=1)
A_ : Dict = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow("gaussian filter with 3x3 mask", gaussianaxa)
imshow("gaussian filter with 5x5 mask", gaussianaxa)
waitKey()
| 696 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=9_9, lowerCamelCase_=6_4, lowerCamelCase_=3_2, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = parent
lowerCamelCase__ : Union[str, Any] = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : List[str] = is_training
lowerCamelCase__ : Optional[Any] = use_input_mask
lowerCamelCase__ : List[Any] = use_token_type_ids
lowerCamelCase__ : List[Any] = use_labels
lowerCamelCase__ : Optional[Any] = vocab_size
lowerCamelCase__ : str = hidden_size
lowerCamelCase__ : Optional[int] = embedding_size
lowerCamelCase__ : List[str] = num_hidden_layers
lowerCamelCase__ : Any = num_attention_heads
lowerCamelCase__ : Any = intermediate_size
lowerCamelCase__ : Union[str, Any] = hidden_act
lowerCamelCase__ : str = hidden_dropout_prob
lowerCamelCase__ : Tuple = attention_probs_dropout_prob
lowerCamelCase__ : Any = max_position_embeddings
lowerCamelCase__ : Any = type_vocab_size
lowerCamelCase__ : List[Any] = type_sequence_label_size
lowerCamelCase__ : Dict = initializer_range
lowerCamelCase__ : Optional[Any] = num_labels
lowerCamelCase__ : Dict = num_choices
lowerCamelCase__ : Tuple = scope
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : List[str] = None
if self.use_input_mask:
lowerCamelCase__ : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Any = None
if self.use_token_type_ids:
lowerCamelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : Any = None
lowerCamelCase__ : Union[str, Any] = None
if self.use_labels:
lowerCamelCase__ : int = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : int = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : str = ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ):
'''simple docstring'''
return MobileBertConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, embedding_size=self.embedding_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=lowerCamelCase_, initializer_range=self.initializer_range, )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = MobileBertModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_, token_type_ids=lowerCamelCase_ )
lowerCamelCase__ : Tuple = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = MobileBertForMaskedLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = MobileBertForNextSentencePrediction(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : str = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = MobileBertForPreTraining(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, next_sentence_label=lowerCamelCase_, )
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = MobileBertForQuestionAnswering(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, start_positions=lowerCamelCase_, end_positions=lowerCamelCase_, )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : int = MobileBertForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.num_labels
lowerCamelCase__ : Optional[int] = MobileBertForTokenClassification(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : int = self.num_choices
lowerCamelCase__ : Dict = MobileBertForMultipleChoice(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = input_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
lowerCamelCase__ : int = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, token_type_ids=lowerCamelCase_, labels=lowerCamelCase_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : List[str] = config_and_inputs
lowerCamelCase__ : Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Dict = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : Tuple = (
{
'feature-extraction': MobileBertModel,
'fill-mask': MobileBertForMaskedLM,
'question-answering': MobileBertForQuestionAnswering,
'text-classification': MobileBertForSequenceClassification,
'token-classification': MobileBertForTokenClassification,
'zero-shot': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : int = True
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=False ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = super()._prepare_for_class(lowerCamelCase_, lowerCamelCase_, return_labels=lowerCamelCase_ )
if return_labels:
if model_class in get_values(lowerCamelCase_ ):
lowerCamelCase__ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=lowerCamelCase_ )
return inputs_dict
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = MobileBertModelTester(self )
lowerCamelCase__ : List[str] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=3_7 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
return torch.tensor(
_lowerCamelCase , dtype=torch.long , device=_lowerCamelCase , )
A_ : Tuple = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = MobileBertModel.from_pretrained('google/mobilebert-uncased' ).to(lowerCamelCase_ )
lowerCamelCase__ : Tuple = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] )
with torch.no_grad():
lowerCamelCase__ : Optional[Any] = model(lowerCamelCase_ )[0]
lowerCamelCase__ : Optional[int] = torch.Size((1, 9, 5_1_2) )
self.assertEqual(output.shape, lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] = torch.tensor(
[
[
[-2.4_736_526e07, 8.2_691_656e04, 1.6_521_838e05],
[-5.7_541_704e-01, 3.9_056_022e00, 4.4_011_507e00],
[2.6_047_359e00, 1.5_677_652e00, -1.7_324_188e-01],
]
], device=lowerCamelCase_, )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
lowerCamelCase__ : Optional[int] = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
lowerCamelCase__ : Any = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 696 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
with open(_lowerCamelCase , 'r' ) as f:
lowerCamelCase__ : Optional[int] = f.readlines()
lowerCamelCase__ : Tuple = f'''class {class_name}('''
lowerCamelCase__ : Union[str, Any] = f'''{4 * ' '}def {test_name}('''
lowerCamelCase__ : int = f'''{8 * ' '}{correct_line.split()[0]}'''
lowerCamelCase__ : List[str] = f'''{16 * ' '}{correct_line.split()[0]}'''
lowerCamelCase__ : Any = False
lowerCamelCase__ : str = False
lowerCamelCase__ : Optional[int] = False
lowerCamelCase__ : List[Any] = False
lowerCamelCase__ : Any = 0
lowerCamelCase__ : Tuple = 0
lowerCamelCase__ : List[Any] = []
for line in lines:
if line.startswith(_lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = True
elif in_class and line.startswith(_lowerCamelCase ):
lowerCamelCase__ : Any = True
elif in_class and in_func and (line.startswith(_lowerCamelCase ) or line.startswith(_lowerCamelCase )):
lowerCamelCase__ : List[str] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
lowerCamelCase__ : str = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
lowerCamelCase__ : List[str] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(f'''{spaces * ' '}{correct_line}''' )
lowerCamelCase__ : Union[str, Any] = False
else:
new_lines.append(_lowerCamelCase )
with open(_lowerCamelCase , 'w' ) as f:
for line in new_lines:
f.write(_lowerCamelCase )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase=None ):
if fail is not None:
with open(_lowerCamelCase , 'r' ) as f:
lowerCamelCase__ : List[str] = {l.strip() for l in f.readlines()}
else:
lowerCamelCase__ : Tuple = None
with open(_lowerCamelCase , 'r' ) as f:
lowerCamelCase__ : Optional[int] = f.readlines()
lowerCamelCase__ : Union[str, Any] = defaultdict(_lowerCamelCase )
for line in correct_lines:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : int = line.split(';' )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if __name__ == "__main__":
A_ : List[str] = argparse.ArgumentParser()
parser.add_argument("--correct_filename", help="filename of tests with expected result")
parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
A_ : Dict = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 696 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
A_ : str = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=1 ):
'''simple docstring'''
lowerCamelCase__ : Any = tokenizer
lowerCamelCase__ : Optional[Any] = dataset
lowerCamelCase__ : int = len(lowerCamelCase_ ) if n_tasks is None else n_tasks
lowerCamelCase__ : Any = n_copies
def __iter__(self ):
'''simple docstring'''
lowerCamelCase__ : Dict = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
lowerCamelCase__ : Optional[int] = self.tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = start_length
lowerCamelCase__ : List[str] = eof_strings
lowerCamelCase__ : List[str] = tokenizer
def __call__(self, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
lowerCamelCase__ : Optional[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = re.split('(%s)' % '|'.join(_lowerCamelCase ) , _lowerCamelCase )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=20 , **_lowerCamelCase ):
lowerCamelCase__ : List[str] = defaultdict(_lowerCamelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_lowerCamelCase ) ):
with torch.no_grad():
lowerCamelCase__ : str = batch['ids'].shape[-1]
lowerCamelCase__ : int = accelerator.unwrap_model(_lowerCamelCase ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=_lowerCamelCase , **_lowerCamelCase )
# each task is generated batch_size times
lowerCamelCase__ : Optional[Any] = batch['task_id'].repeat(_lowerCamelCase )
lowerCamelCase__ : List[Any] = accelerator.pad_across_processes(
_lowerCamelCase , dim=1 , pad_index=tokenizer.pad_token_id )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
lowerCamelCase__ : List[Any] = generated_tokens.cpu().numpy()
lowerCamelCase__ : Union[str, Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_lowerCamelCase , _lowerCamelCase ):
gen_token_dict[task].append(_lowerCamelCase )
lowerCamelCase__ : str = [[] for _ in range(_lowerCamelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
lowerCamelCase__ : Optional[Any] = tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
code_gens[task].append(remove_last_block(_lowerCamelCase ) )
return code_gens
def lowerCamelCase_ ( ):
# Setup configuration
lowerCamelCase__ : int = HfArgumentParser(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
lowerCamelCase__ : List[str] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
lowerCamelCase__ : Tuple = 'false'
if args.num_workers is None:
lowerCamelCase__ : List[Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
lowerCamelCase__ : List[Any] = Accelerator()
set_seed(args.seed , device_specific=_lowerCamelCase )
# Load model and tokenizer
lowerCamelCase__ : Any = AutoTokenizer.from_pretrained(args.model_ckpt )
lowerCamelCase__ : Optional[int] = tokenizer.eos_token
lowerCamelCase__ : Any = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
lowerCamelCase__ : Optional[Any] = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , _lowerCamelCase , _lowerCamelCase )] ),
}
# Load evaluation dataset and metric
lowerCamelCase__ : Any = load_dataset('openai_humaneval' )
lowerCamelCase__ : Optional[int] = load_metric('code_eval' )
lowerCamelCase__ : List[Any] = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
lowerCamelCase__ : Optional[int] = args.n_samples // args.batch_size
lowerCamelCase__ : Tuple = TokenizedDataset(_lowerCamelCase , human_eval['test'] , n_copies=_lowerCamelCase , n_tasks=_lowerCamelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
lowerCamelCase__ : Union[str, Any] = DataLoader(_lowerCamelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
lowerCamelCase__ : List[Any] = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
lowerCamelCase__ , lowerCamelCase__ : str = accelerator.prepare(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Any = complete_code(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , n_tasks=_lowerCamelCase , batch_size=args.batch_size , **_lowerCamelCase , )
if accelerator.is_main_process:
lowerCamelCase__ : List[str] = []
for task in tqdm(range(_lowerCamelCase ) ):
lowerCamelCase__ : int = human_eval['test'][task]['test']
lowerCamelCase__ : Union[str, Any] = f'''check({human_eval['test'][task]['entry_point']})'''
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
lowerCamelCase__ , lowerCamelCase__ : Any = code_eval_metric.compute(
references=_lowerCamelCase , predictions=_lowerCamelCase , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 696 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=3, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=2_2_4, lowerCamelCase_=1_0_0_0, lowerCamelCase_=[3, 3, 6, 4], lowerCamelCase_=[4_8, 5_6, 1_1_2, 2_2_0], ):
'''simple docstring'''
lowerCamelCase__ : Any = parent
lowerCamelCase__ : List[str] = batch_size
lowerCamelCase__ : List[str] = num_channels
lowerCamelCase__ : Any = is_training
lowerCamelCase__ : Optional[Any] = use_labels
lowerCamelCase__ : Optional[int] = hidden_dropout_prob
lowerCamelCase__ : Union[str, Any] = attention_probs_dropout_prob
lowerCamelCase__ : List[Any] = num_labels
lowerCamelCase__ : Any = image_size
lowerCamelCase__ : List[Any] = layer_depths
lowerCamelCase__ : Dict = embed_dims
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ : Dict = None
if self.use_labels:
lowerCamelCase__ : Union[str, Any] = ids_tensor([self.batch_size], self.num_labels )
lowerCamelCase__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def a__ (self ):
'''simple docstring'''
return SwiftFormerConfig(
depths=self.layer_depths, embed_dims=self.embed_dims, mlp_ratio=4, downsamples=[True, True, True, True], hidden_act='gelu', num_labels=self.num_labels, down_patch_size=3, down_stride=2, down_pad=1, drop_rate=0.0, drop_path_rate=0.0, use_layer_scale=lowerCamelCase_, layer_scale_init_value=1e-5, )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : int = SwiftFormerModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dims[-1], 7, 7) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.num_labels
lowerCamelCase__ : Optional[Any] = SwiftFormerForImageClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
lowerCamelCase__ : Any = SwiftFormerForImageClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ : List[Any] = model(lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self ):
'''simple docstring'''
((lowerCamelCase__) , (lowerCamelCase__) , (lowerCamelCase__)) : Optional[Any] = self.prepare_config_and_inputs()
lowerCamelCase__ : List[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : str = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
lowerCamelCase__ : Tuple = (
{'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ : Optional[int] = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Optional[Any] = False
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Optional[Any] = False
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = SwiftFormerModelTester(self )
lowerCamelCase__ : Dict = ConfigTester(
self, config_class=lowerCamelCase_, has_text_modality=lowerCamelCase_, hidden_size=3_7, num_attention_heads=1_2, num_hidden_layers=1_2, )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='SwiftFormer does not use inputs_embeds' )
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : Union[str, Any] = model_class(lowerCamelCase_ )
lowerCamelCase__ : str = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase_, nn.Linear ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : Optional[int] = model_class(lowerCamelCase_ )
lowerCamelCase__ : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ : List[Any] = [*signature.parameters.keys()]
lowerCamelCase__ : Optional[int] = ['pixel_values']
self.assertListEqual(arg_names[:1], lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_ )
@slow
def a__ (self ):
'''simple docstring'''
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ : Dict = SwiftFormerModel.from_pretrained(lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
@unittest.skip(reason='SwiftFormer does not output attentions' )
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
def check_hidden_states_output(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
lowerCamelCase__ : Optional[Any] = model_class(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
with torch.no_grad():
lowerCamelCase__ : Any = model(**self._prepare_for_class(lowerCamelCase_, lowerCamelCase_ ) )
lowerCamelCase__ : Any = outputs.hidden_states
lowerCamelCase__ : Dict = 8
self.assertEqual(len(lowerCamelCase_ ), lowerCamelCase_ ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(lowerCamelCase_ ) ):
self.assertEqual(
hidden_states[i].shape, torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ), )
lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : Optional[int] = True
check_hidden_states_output(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase__ : int = True
check_hidden_states_output(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
def _config_zero_init(lowerCamelCase_ ):
lowerCamelCase__ : Optional[int] = copy.deepcopy(lowerCamelCase_ )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(lowerCamelCase_, lowerCamelCase_, 1e-10 )
if isinstance(getattr(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ), lowerCamelCase_ ):
lowerCamelCase__ : List[Any] = _config_zero_init(getattr(lowerCamelCase_, lowerCamelCase_ ) )
setattr(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
return configs_no_init
lowerCamelCase__ , lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Any = _config_zero_init(lowerCamelCase_ )
for model_class in self.all_model_classes:
lowerCamelCase__ : Union[str, Any] = model_class(config=lowerCamelCase_ )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item(), [0.0, 1.0], msg=f'''Parameter {name} of model {model_class} seems not properly initialized''', )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def a__ (self ):
'''simple docstring'''
pass
def lowerCamelCase_ ( ):
lowerCamelCase__ : List[str] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def a__ (self ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs' ) if is_vision_available() else None
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs' ).to(lowerCamelCase_ )
lowerCamelCase__ : str = self.default_image_processor
lowerCamelCase__ : Union[str, Any] = prepare_img()
lowerCamelCase__ : Tuple = image_processor(images=lowerCamelCase_, return_tensors='pt' ).to(lowerCamelCase_ )
# forward pass
with torch.no_grad():
lowerCamelCase__ : Tuple = model(**lowerCamelCase_ )
# verify the logits
lowerCamelCase__ : Tuple = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, lowerCamelCase_ )
lowerCamelCase__ : List[str] = torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]] ).to(lowerCamelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3], lowerCamelCase_, atol=1e-4 ) )
| 696 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : str = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
requires_backends(self, ['speech'] )
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
requires_backends(self, ['speech'] )
| 696 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 10**-10 ):
lowerCamelCase__ : Optional[int] = a
while True:
lowerCamelCase__ : Any = Decimal(_lowerCamelCase ) - (
Decimal(eval(_lowerCamelCase ) ) / Decimal(eval(str(diff(_lowerCamelCase ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(_lowerCamelCase ) ) < precision: # noqa: S307
return float(_lowerCamelCase )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")
# Find root of polynomial
print(f"The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}")
# Find Square Root of 5
print(f"The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}")
# Exponential Roots
print(f"The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}")
| 696 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = 0
while number > 0:
lowerCamelCase__ : List[str] = number % 10
sum_of_digits += last_digit
lowerCamelCase__ : str = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def lowerCamelCase_ ( _lowerCamelCase = 100 ):
lowerCamelCase__ : Union[str, Any] = factorial(_lowerCamelCase )
lowerCamelCase__ : List[Any] = split_and_add(_lowerCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 696 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Dict = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = [
"TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TimeSeriesTransformerForPrediction",
"TimeSeriesTransformerModel",
"TimeSeriesTransformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
A_ : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A_ : Dict = "pt"
elif is_tf_available():
A_ : Union[str, Any] = "tf"
else:
A_ : List[str] = "jax"
class a_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = PerceiverTokenizer
lowerCamelCase__ : Optional[Any] = False
def a__ (self ):
'''simple docstring'''
super().setUp()
lowerCamelCase__ : int = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def a__ (self ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver' )
def a__ (self, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname, **lowerCamelCase_ )
def a__ (self, lowerCamelCase_, lowerCamelCase_=False, lowerCamelCase_=2_0, lowerCamelCase_=5 ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = []
for i in range(len(lowerCamelCase_ ) ):
try:
lowerCamelCase__ : Any = tokenizer.decode([i], clean_up_tokenization_spaces=lowerCamelCase_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCamelCase__ : Any = list(filter(lambda lowerCamelCase_ : re.match(r'^[ a-zA-Z]+$', t[1] ), lowerCamelCase_ ) )
lowerCamelCase__ : Union[str, Any] = list(filter(lambda lowerCamelCase_ : [t[0]] == tokenizer.encode(t[1], add_special_tokens=lowerCamelCase_ ), lowerCamelCase_ ) )
if max_length is not None and len(lowerCamelCase_ ) > max_length:
lowerCamelCase__ : int = toks[:max_length]
if min_length is not None and len(lowerCamelCase_ ) < min_length and len(lowerCamelCase_ ) > 0:
while len(lowerCamelCase_ ) < min_length:
lowerCamelCase__ : Dict = toks + toks
# toks_str = [t[1] for t in toks]
lowerCamelCase__ : int = [t[0] for t in toks]
# Ensure consistency
lowerCamelCase__ : Optional[int] = tokenizer.decode(lowerCamelCase_, clean_up_tokenization_spaces=lowerCamelCase_ )
if " " not in output_txt and len(lowerCamelCase_ ) > 1:
lowerCamelCase__ : List[Any] = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=lowerCamelCase_ )
+ ' '
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=lowerCamelCase_ )
)
if with_prefix_space:
lowerCamelCase__ : Optional[Any] = ' ' + output_txt
lowerCamelCase__ : List[Any] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
return output_txt, output_ids
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = 'Unicode €.'
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_ )
lowerCamelCase__ : Dict = [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : int = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]Unicode €.[SEP]' )
lowerCamelCase__ : List[str] = tokenizer('e è é ê ë' )
lowerCamelCase__ : Dict = [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
self.assertEqual(encoded['input_ids'], lowerCamelCase_ )
# decoding
lowerCamelCase__ : Any = tokenizer.decode(lowerCamelCase_ )
self.assertEqual(lowerCamelCase_, '[CLS]e è é ê ë[SEP]' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ), '[CLS]e è é ê ë[SEP]' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : Union[str, Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
lowerCamelCase__ : List[Any] = [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
# fmt: on
lowerCamelCase__ : Optional[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
if FRAMEWORK != "jax":
lowerCamelCase__ : List[str] = list(batch.input_ids.numpy()[0] )
else:
lowerCamelCase__ : int = list(batch.input_ids.tolist()[0] )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertEqual((2, 3_8), batch.input_ids.shape )
self.assertEqual((2, 3_8), batch.attention_mask.shape )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.perceiver_tokenizer
lowerCamelCase__ : List[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCamelCase__ : List[Any] = tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors=lowerCamelCase_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids', lowerCamelCase_ )
self.assertIn('attention_mask', lowerCamelCase_ )
self.assertNotIn('decoder_input_ids', lowerCamelCase_ )
self.assertNotIn('decoder_attention_mask', lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.perceiver_tokenizer
lowerCamelCase__ : int = [
'Summary of the text.',
'Another summary.',
]
lowerCamelCase__ : str = tokenizer(
text_target=lowerCamelCase_, max_length=3_2, padding='max_length', truncation=lowerCamelCase_, return_tensors=lowerCamelCase_ )
self.assertEqual(3_2, targets['input_ids'].shape[1] )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length, 4_2 )
# Now let's start the test
lowerCamelCase__ : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : str = ' He is very happy, UNwant\u00E9d,running'
lowerCamelCase__ : str = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : str = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
shutil.rmtree(lowerCamelCase_ )
lowerCamelCase__ : List[Any] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : Union[str, Any] = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
lowerCamelCase__ : List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
lowerCamelCase__ : List[str] = tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
tokenizer.save_pretrained(lowerCamelCase_ )
lowerCamelCase__ : int = tokenizer.__class__.from_pretrained(lowerCamelCase_ )
lowerCamelCase__ : Tuple = after_tokenizer.encode(lowerCamelCase_, add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_, lowerCamelCase_ )
self.assertIn('new_additional_special_token', after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length, 4_2 )
lowerCamelCase__ : List[Any] = tokenizer.__class__.from_pretrained(lowerCamelCase_, model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length, 4_3 )
shutil.rmtree(lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : Optional[Any] = json.load(lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), encoding='utf-8' ) as json_file:
lowerCamelCase__ : List[str] = json.load(lowerCamelCase_ )
lowerCamelCase__ : Any = [f'''<extra_id_{i}>''' for i in range(1_2_5 )]
lowerCamelCase__ : Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
lowerCamelCase__ : List[str] = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(lowerCamelCase_, 'special_tokens_map.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
with open(os.path.join(lowerCamelCase_, 'tokenizer_config.json' ), 'w', encoding='utf-8' ) as outfile:
json.dump(lowerCamelCase_, lowerCamelCase_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCamelCase__ : Dict = tokenizer_class.from_pretrained(
lowerCamelCase_, )
self.assertIn(
'an_additional_special_token', tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['an_additional_special_token'], tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ), )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCamelCase__ : Optional[Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token', lstrip=lowerCamelCase_ )]
lowerCamelCase__ : Any = tokenizer_class.from_pretrained(
lowerCamelCase_, additional_special_tokens=lowerCamelCase_, )
self.assertIn('a_new_additional_special_token', tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'], tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ), )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_7_8] ), '�' )
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
pass
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.get_tokenizers(fast=lowerCamelCase_, do_lower_case=lowerCamelCase_ )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
lowerCamelCase__ : Tuple = ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
lowerCamelCase__ : List[str] = tokenizer.convert_tokens_to_string(lowerCamelCase_ )
self.assertIsInstance(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a_ ( snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : List[str] = ['image_processor', 'tokenizer']
lowerCamelCase__ : Dict = 'AutoImageProcessor'
lowerCamelCase__ : List[str] = 'AutoTokenizer'
def __init__(self, lowerCamelCase_=None, lowerCamelCase_=None, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : str = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.', lowerCamelCase_, )
lowerCamelCase__ : List[str] = kwargs.pop('feature_extractor' )
lowerCamelCase__ : Dict = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(lowerCamelCase_, lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = self.image_processor
lowerCamelCase__ : Union[str, Any] = False
def __call__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*lowerCamelCase_, **lowerCamelCase_ )
lowerCamelCase__ : List[Any] = kwargs.pop('images', lowerCamelCase_ )
lowerCamelCase__ : Any = kwargs.pop('text', lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
lowerCamelCase__ : Any = args[0]
lowerCamelCase__ : Tuple = args[1:]
if images is None and text is None:
raise ValueError('You need to specify either an `images` or `text` input to process.' )
if images is not None:
lowerCamelCase__ : Optional[Any] = self.image_processor(lowerCamelCase_, *lowerCamelCase_, **lowerCamelCase_ )
if text is not None:
lowerCamelCase__ : Optional[int] = self.tokenizer(lowerCamelCase_, **lowerCamelCase_ )
if text is None:
return inputs
elif images is None:
return encodings
else:
lowerCamelCase__ : Dict = encodings['input_ids']
return inputs
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase_, **lowerCamelCase_ )
def a__ (self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase_, **lowerCamelCase_ )
@contextmanager
def a__ (self ):
'''simple docstring'''
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your images inputs, or in a separate call.' )
lowerCamelCase__ : Union[str, Any] = True
lowerCamelCase__ : Union[str, Any] = self.tokenizer
yield
lowerCamelCase__ : Optional[int] = self.image_processor
lowerCamelCase__ : str = False
def a__ (self, lowerCamelCase_, lowerCamelCase_=False, lowerCamelCase_=None ):
'''simple docstring'''
if added_vocab is None:
lowerCamelCase__ : Dict = self.tokenizer.get_added_vocab()
lowerCamelCase__ : Optional[Any] = {}
while tokens:
lowerCamelCase__ : Dict = re.search(r'<s_(.*?)>', lowerCamelCase_, re.IGNORECASE )
if start_token is None:
break
lowerCamelCase__ : List[Any] = start_token.group(1 )
lowerCamelCase__ : Dict = re.search(rf'''</s_{key}>''', lowerCamelCase_, re.IGNORECASE )
lowerCamelCase__ : Tuple = start_token.group()
if end_token is None:
lowerCamelCase__ : Optional[int] = tokens.replace(lowerCamelCase_, '' )
else:
lowerCamelCase__ : str = end_token.group()
lowerCamelCase__ : str = re.escape(lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = re.escape(lowerCamelCase_ )
lowerCamelCase__ : Any = re.search(f'''{start_token_escaped}(.*?){end_token_escaped}''', lowerCamelCase_, re.IGNORECASE )
if content is not None:
lowerCamelCase__ : Dict = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
lowerCamelCase__ : Any = self.tokenajson(lowerCamelCase_, is_inner_value=lowerCamelCase_, added_vocab=lowerCamelCase_ )
if value:
if len(lowerCamelCase_ ) == 1:
lowerCamelCase__ : List[Any] = value[0]
lowerCamelCase__ : Dict = value
else: # leaf nodes
lowerCamelCase__ : Dict = []
for leaf in content.split(r'<sep/>' ):
lowerCamelCase__ : List[str] = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
lowerCamelCase__ : Union[str, Any] = leaf[1:-2] # for categorical special tokens
output[key].append(lowerCamelCase_ )
if len(output[key] ) == 1:
lowerCamelCase__ : Optional[int] = output[key][0]
lowerCamelCase__ : Any = tokens[tokens.find(lowerCamelCase_ ) + len(lowerCamelCase_ ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:], is_inner_value=lowerCamelCase_, added_vocab=lowerCamelCase_ )
if len(lowerCamelCase_ ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def a__ (self ):
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.', lowerCamelCase_, )
return self.image_processor_class
@property
def a__ (self ):
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.', lowerCamelCase_, )
return self.image_processor
| 696 |
"""simple docstring"""
from math import pi, sqrt, tan
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
lowerCamelCase__ : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(_lowerCamelCase , 2 ) * torus_radius * tube_radius
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def lowerCamelCase_ ( _lowerCamelCase ):
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
lowerCamelCase__ : Dict = (sidea + sidea + sidea) / 2
lowerCamelCase__ : str = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def lowerCamelCase_ ( _lowerCamelCase ):
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \
length of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 696 | 1 |
"""simple docstring"""
import os
import sys
import unittest
A_ : Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
A_ : int = os.path.join(git_repo_path, "src", "transformers")
A_ : Union[str, Any] = "\n{0} = None\n"
A_ : int = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n"
A_ : Tuple = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
class a_ ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")' )
self.assertIsNone(lowerCamelCase_ )
lowerCamelCase__ : Tuple = find_backend(' if not is_tokenizers_available():' )
self.assertEqual(lowerCamelCase_, 'tokenizers' )
lowerCamelCase__ : Any = find_backend(' if not is_tensorflow_text_available():' )
self.assertEqual(lowerCamelCase_, 'tensorflow_text' )
lowerCamelCase__ : Tuple = find_backend(' if not (is_sentencepiece_available() and is_tokenizers_available()):' )
self.assertEqual(lowerCamelCase_, 'sentencepiece_and_tokenizers' )
lowerCamelCase__ : Optional[int] = find_backend(
' if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
self.assertEqual(lowerCamelCase_, 'sentencepiece_and_tensorflow_text' )
lowerCamelCase__ : str = find_backend(
' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
self.assertEqual(lowerCamelCase_, 'sentencepiece_and_tokenizers_and_vision' )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch', lowerCamelCase_ )
self.assertIn('tensorflow_text', lowerCamelCase_ )
self.assertIn('sentencepiece_and_tokenizers', lowerCamelCase_ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('BertModel', objects['torch'] )
self.assertIn('TFBertModel', objects['tf'] )
self.assertIn('FlaxBertModel', objects['flax'] )
self.assertIn('BertModel', objects['torch'] )
self.assertIn('TFBertTokenizer', objects['tensorflow_text'] )
self.assertIn('convert_slow_tokenizer', objects['sentencepiece_and_tokenizers'] )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = create_dummy_object('CONSTANT', '\'torch\'' )
self.assertEqual(lowerCamelCase_, '\nCONSTANT = None\n' )
lowerCamelCase__ : List[Any] = create_dummy_object('function', '\'torch\'' )
self.assertEqual(
lowerCamelCase_, '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
lowerCamelCase__ : str = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n'
lowerCamelCase__ : Dict = create_dummy_object('FakeClass', '\'torch\'' )
self.assertEqual(lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n'
lowerCamelCase__ : Tuple = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'], lowerCamelCase_ )
| 696 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_=1_3, lowerCamelCase_=7, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=True, lowerCamelCase_=9_9, lowerCamelCase_=6_4, lowerCamelCase_=5, lowerCamelCase_=4, lowerCamelCase_=3_7, lowerCamelCase_="gelu", lowerCamelCase_=0.1, lowerCamelCase_=0.1, lowerCamelCase_=5_1_2, lowerCamelCase_=1_6, lowerCamelCase_=2, lowerCamelCase_=0.02, lowerCamelCase_=3, lowerCamelCase_=4, lowerCamelCase_=None, ):
'''simple docstring'''
lowerCamelCase__ : Dict = parent
lowerCamelCase__ : Tuple = batch_size
lowerCamelCase__ : List[Any] = seq_length
lowerCamelCase__ : List[Any] = is_training
lowerCamelCase__ : str = use_input_mask
lowerCamelCase__ : Optional[Any] = use_token_type_ids
lowerCamelCase__ : Any = use_labels
lowerCamelCase__ : Optional[int] = vocab_size
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : Optional[int] = num_hidden_layers
lowerCamelCase__ : List[Any] = num_attention_heads
lowerCamelCase__ : Union[str, Any] = intermediate_size
lowerCamelCase__ : List[str] = hidden_act
lowerCamelCase__ : Union[str, Any] = hidden_dropout_prob
lowerCamelCase__ : Optional[int] = attention_probs_dropout_prob
lowerCamelCase__ : Dict = max_position_embeddings
lowerCamelCase__ : Dict = type_vocab_size
lowerCamelCase__ : Union[str, Any] = type_sequence_label_size
lowerCamelCase__ : List[Any] = initializer_range
lowerCamelCase__ : List[Any] = num_labels
lowerCamelCase__ : Union[str, Any] = num_choices
lowerCamelCase__ : List[str] = scope
lowerCamelCase__ : Dict = vocab_size - 1
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Optional[Any] = None
if self.use_input_mask:
lowerCamelCase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Any = None
if self.use_labels:
lowerCamelCase__ : Tuple = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : str = self.get_config()
return config, input_ids, input_mask, token_labels
def a__ (self ):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=lowerCamelCase_, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[str] = self.prepare_config_and_inputs()
lowerCamelCase__ : Optional[Any] = True
return config, input_ids, input_mask, token_labels
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = GPTNeoXModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : List[Any] = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : int = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.num_labels
lowerCamelCase__ : Optional[Any] = GPTNeoXForQuestionAnswering(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_ )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : str = self.num_labels
lowerCamelCase__ : Optional[int] = GPTNeoXForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Dict = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : str = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : List[Any] = GPTNeoXForTokenClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCamelCase__ : Tuple = model(lowerCamelCase_, attention_mask=lowerCamelCase_, labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = True
lowerCamelCase__ : List[str] = GPTNeoXForCausalLM(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
# first forward pass
lowerCamelCase__ : Optional[int] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, use_cache=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase__ : str = ids_tensor((self.batch_size, 3), config.vocab_size )
lowerCamelCase__ : List[Any] = ids_tensor((self.batch_size, 3), vocab_size=2 )
# append to next input_ids and
lowerCamelCase__ : Tuple = torch.cat([input_ids, next_tokens], dim=-1 )
lowerCamelCase__ : Tuple = torch.cat([input_mask, next_mask], dim=-1 )
lowerCamelCase__ : List[str] = model(lowerCamelCase_, attention_mask=lowerCamelCase_, output_hidden_states=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] = output_from_no_past['hidden_states'][0]
lowerCamelCase__ : Optional[Any] = model(
lowerCamelCase_, attention_mask=lowerCamelCase_, past_key_values=lowerCamelCase_, output_hidden_states=lowerCamelCase_, )['hidden_states'][0]
# select random slice
lowerCamelCase__ : Dict = ids_tensor((1,), output_from_past.shape[-1] ).item()
lowerCamelCase__ : Optional[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-3 ) )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = config_and_inputs
lowerCamelCase__ : List[str] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : int = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ : Dict = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : Dict = False
lowerCamelCase__ : Optional[int] = False
lowerCamelCase__ : Any = False
lowerCamelCase__ : Dict = False
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Any = GPTNeoXModelTester(self )
lowerCamelCase__ : Union[str, Any] = ConfigTester(self, config_class=lowerCamelCase_, hidden_size=6_4, num_attention_heads=8 )
def a__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCamelCase__ : Optional[Any] = None
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
@unittest.skip(reason='Feed forward chunking is not implemented' )
def a__ (self ):
'''simple docstring'''
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Optional[Any] = ids_tensor([1, 1_0], config.vocab_size )
lowerCamelCase__ : Tuple = ids_tensor([1, int(config.max_position_embeddings * 1.5 )], config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Any = GPTNeoXModel(lowerCamelCase_ )
original_model.to(lowerCamelCase_ )
original_model.eval()
lowerCamelCase__ : List[Any] = original_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = original_model(lowerCamelCase_ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
lowerCamelCase__ : Optional[int] = {'type': scaling_type, 'factor': 10.0}
lowerCamelCase__ : int = GPTNeoXModel(lowerCamelCase_ )
scaled_model.to(lowerCamelCase_ )
scaled_model.eval()
lowerCamelCase__ : Tuple = scaled_model(lowerCamelCase_ ).last_hidden_state
lowerCamelCase__ : Optional[int] = scaled_model(lowerCamelCase_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase_, lowerCamelCase_, atol=1e-5 ) )
@require_torch
class a_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ):
'''simple docstring'''
lowerCamelCase__ : str = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' )
for checkpointing in [True, False]:
lowerCamelCase__ : Optional[Any] = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = tokenizer('My favorite food is', return_tensors='pt' ).to(lowerCamelCase_ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
lowerCamelCase__ : Dict = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'
lowerCamelCase__ : Dict = model.generate(**lowerCamelCase_, do_sample=lowerCamelCase_, max_new_tokens=2_0 )
lowerCamelCase__ : Optional[Any] = tokenizer.batch_decode(lowerCamelCase_ )[0]
self.assertEqual(lowerCamelCase_, lowerCamelCase_ )
| 696 | 1 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
A_ : str = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=1 ):
'''simple docstring'''
lowerCamelCase__ : Any = tokenizer
lowerCamelCase__ : Optional[Any] = dataset
lowerCamelCase__ : int = len(lowerCamelCase_ ) if n_tasks is None else n_tasks
lowerCamelCase__ : Any = n_copies
def __iter__(self ):
'''simple docstring'''
lowerCamelCase__ : Dict = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
lowerCamelCase__ : Optional[int] = self.tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = start_length
lowerCamelCase__ : List[str] = eof_strings
lowerCamelCase__ : List[str] = tokenizer
def __call__(self, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
lowerCamelCase__ : Optional[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = re.split('(%s)' % '|'.join(_lowerCamelCase ) , _lowerCamelCase )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=20 , **_lowerCamelCase ):
lowerCamelCase__ : List[str] = defaultdict(_lowerCamelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_lowerCamelCase ) ):
with torch.no_grad():
lowerCamelCase__ : str = batch['ids'].shape[-1]
lowerCamelCase__ : int = accelerator.unwrap_model(_lowerCamelCase ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=_lowerCamelCase , **_lowerCamelCase )
# each task is generated batch_size times
lowerCamelCase__ : Optional[Any] = batch['task_id'].repeat(_lowerCamelCase )
lowerCamelCase__ : List[Any] = accelerator.pad_across_processes(
_lowerCamelCase , dim=1 , pad_index=tokenizer.pad_token_id )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
lowerCamelCase__ : List[Any] = generated_tokens.cpu().numpy()
lowerCamelCase__ : Union[str, Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_lowerCamelCase , _lowerCamelCase ):
gen_token_dict[task].append(_lowerCamelCase )
lowerCamelCase__ : str = [[] for _ in range(_lowerCamelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
lowerCamelCase__ : Optional[Any] = tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
code_gens[task].append(remove_last_block(_lowerCamelCase ) )
return code_gens
def lowerCamelCase_ ( ):
# Setup configuration
lowerCamelCase__ : int = HfArgumentParser(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
lowerCamelCase__ : List[str] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
lowerCamelCase__ : Tuple = 'false'
if args.num_workers is None:
lowerCamelCase__ : List[Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
lowerCamelCase__ : List[Any] = Accelerator()
set_seed(args.seed , device_specific=_lowerCamelCase )
# Load model and tokenizer
lowerCamelCase__ : Any = AutoTokenizer.from_pretrained(args.model_ckpt )
lowerCamelCase__ : Optional[int] = tokenizer.eos_token
lowerCamelCase__ : Any = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
lowerCamelCase__ : Optional[Any] = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , _lowerCamelCase , _lowerCamelCase )] ),
}
# Load evaluation dataset and metric
lowerCamelCase__ : Any = load_dataset('openai_humaneval' )
lowerCamelCase__ : Optional[int] = load_metric('code_eval' )
lowerCamelCase__ : List[Any] = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
lowerCamelCase__ : Optional[int] = args.n_samples // args.batch_size
lowerCamelCase__ : Tuple = TokenizedDataset(_lowerCamelCase , human_eval['test'] , n_copies=_lowerCamelCase , n_tasks=_lowerCamelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
lowerCamelCase__ : Union[str, Any] = DataLoader(_lowerCamelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
lowerCamelCase__ : List[Any] = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
lowerCamelCase__ , lowerCamelCase__ : str = accelerator.prepare(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Any = complete_code(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , n_tasks=_lowerCamelCase , batch_size=args.batch_size , **_lowerCamelCase , )
if accelerator.is_main_process:
lowerCamelCase__ : List[str] = []
for task in tqdm(range(_lowerCamelCase ) ):
lowerCamelCase__ : int = human_eval['test'][task]['test']
lowerCamelCase__ : Union[str, Any] = f'''check({human_eval['test'][task]['entry_point']})'''
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
lowerCamelCase__ , lowerCamelCase__ : Any = code_eval_metric.compute(
references=_lowerCamelCase , predictions=_lowerCamelCase , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 696 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A_ : Dict = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
A_ : List[Any] = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
A_ : Union[str, Any] = spec.loader.load_module()
A_ : int = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
A_ : Optional[int] = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
A_ : str = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def lowerCamelCase_ ( ):
lowerCamelCase__ : Dict = []
for config_class in list(CONFIG_MAPPING.values() ):
lowerCamelCase__ : Dict = False
# source code of `config_class`
lowerCamelCase__ : str = inspect.getsource(_lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = _re_checkpoint.findall(_lowerCamelCase )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
lowerCamelCase__ : Any = f'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
lowerCamelCase__ : Any = True
break
lowerCamelCase__ : Dict = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
lowerCamelCase__ : Optional[Any] = '\n'.join(sorted(_lowerCamelCase ) )
raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 696 | 1 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, ):
'''simple docstring'''
super().__init__()
lowerCamelCase__ : Dict = value_function
lowerCamelCase__ : int = unet
lowerCamelCase__ : Union[str, Any] = scheduler
lowerCamelCase__ : int = env
lowerCamelCase__ : List[Any] = env.get_dataset()
lowerCamelCase__ : Dict = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
lowerCamelCase__ : Optional[int] = {}
for key in self.data.keys():
try:
lowerCamelCase__ : Tuple = self.data[key].std()
except: # noqa: E722
pass
lowerCamelCase__ : Optional[Any] = env.observation_space.shape[0]
lowerCamelCase__ : List[str] = env.action_space.shape[0]
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def a__ (self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def a__ (self, lowerCamelCase_ ):
'''simple docstring'''
if type(lowerCamelCase_ ) is dict:
return {k: self.to_torch(lowerCamelCase_ ) for k, v in x_in.items()}
elif torch.is_tensor(lowerCamelCase_ ):
return x_in.to(self.unet.device )
return torch.tensor(lowerCamelCase_, device=self.unet.device )
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
for key, val in cond.items():
lowerCamelCase__ : Optional[Any] = val.clone()
return x_in
def a__ (self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Tuple = x.shape[0]
lowerCamelCase__ : Tuple = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowerCamelCase__ : Dict = torch.full((batch_size,), lowerCamelCase_, device=self.unet.device, dtype=torch.long )
for _ in range(lowerCamelCase_ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowerCamelCase__ : str = self.value_function(x.permute(0, 2, 1 ), lowerCamelCase_ ).sample
lowerCamelCase__ : Union[str, Any] = torch.autograd.grad([y.sum()], [x] )[0]
lowerCamelCase__ : Optional[int] = self.scheduler._get_variance(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] = torch.exp(0.5 * posterior_variance )
lowerCamelCase__ : Tuple = model_std * grad
lowerCamelCase__ : str = 0
lowerCamelCase__ : Dict = x.detach()
lowerCamelCase__ : Dict = x + scale * grad
lowerCamelCase__ : Optional[int] = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : Tuple = self.unet(x.permute(0, 2, 1 ), lowerCamelCase_ ).sample.permute(0, 2, 1 )
# TODO: verify deprecation of this kwarg
lowerCamelCase__ : Optional[Any] = self.scheduler.step(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, predict_epsilon=lowerCamelCase_ )['prev_sample']
# apply conditions to the trajectory (set the initial state)
lowerCamelCase__ : Any = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : List[str] = self.to_torch(lowerCamelCase_ )
return x, y
def __call__(self, lowerCamelCase_, lowerCamelCase_=6_4, lowerCamelCase_=3_2, lowerCamelCase_=2, lowerCamelCase_=0.1 ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.normalize(lowerCamelCase_, 'observations' )
lowerCamelCase__ : List[str] = obs[None].repeat(lowerCamelCase_, axis=0 )
lowerCamelCase__ : str = {0: self.to_torch(lowerCamelCase_ )}
lowerCamelCase__ : Optional[Any] = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowerCamelCase__ : List[Any] = randn_tensor(lowerCamelCase_, device=self.unet.device )
lowerCamelCase__ : int = self.reset_xa(lowerCamelCase_, lowerCamelCase_, self.action_dim )
lowerCamelCase__ : List[str] = self.to_torch(lowerCamelCase_ )
# run the diffusion process
lowerCamelCase__ , lowerCamelCase__ : List[str] = self.run_diffusion(lowerCamelCase_, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ )
# sort output trajectories by value
lowerCamelCase__ : Union[str, Any] = y.argsort(0, descending=lowerCamelCase_ ).squeeze()
lowerCamelCase__ : List[str] = x[sorted_idx]
lowerCamelCase__ : Optional[Any] = sorted_values[:, :, : self.action_dim]
lowerCamelCase__ : Union[str, Any] = actions.detach().cpu().numpy()
lowerCamelCase__ : Union[str, Any] = self.de_normalize(lowerCamelCase_, key='actions' )
# select the action with the highest value
if y is not None:
lowerCamelCase__ : str = 0
else:
# if we didn't run value guiding, select a random action
lowerCamelCase__ : Optional[Any] = np.random.randint(0, lowerCamelCase_ )
lowerCamelCase__ : Tuple = denorm_actions[selected_index, 0]
return denorm_actions
| 696 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = ["LlamaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = ["LlamaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"LlamaForCausalLM",
"LlamaModel",
"LlamaPreTrainedModel",
"LlamaForSequenceClassification",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
A_ : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 696 | 1 |
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